NEURAL PROGRAMMING

Organization Name

Inventor(s)

Joao Ferdinando Gomes De Freitas of London (GB)

NEURAL PROGRAMMING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240177001 titled 'NEURAL PROGRAMMING

The patent application describes methods, systems, and apparatus for neural programming, including using a core recurrent neural network to process inputs and generate outputs that determine program flow and arguments.

Processing current neural network input using a core recurrent neural network
Determining whether to end a currently invoked program or call a next program based on the neural network output
Determining the contents of arguments for the next program to be called
Receiving a representation of the current state of the environment
Generating a next neural network input based on the embedding for the next program and the current state of the environment

Potential Applications: - Artificial intelligence systems - Robotics - Automation processes

Problems Solved: - Efficient program flow control - Adaptive decision-making based on environmental inputs

Benefits: - Improved program efficiency - Enhanced adaptability in dynamic environments

Commercial Applications: Neural programming technology can be utilized in various industries such as autonomous vehicles, smart home systems, and industrial automation.

Prior Art: There may be existing technologies related to neural programming and decision-making systems in AI and robotics fields.

Frequently Updated Research: Stay updated on advancements in neural programming algorithms and applications for real-time decision-making processes.

Questions about Neural Programming:

Question 1: How does neural programming differ from traditional programming methods? Answer: Neural programming utilizes neural networks to make decisions based on inputs and generate outputs, allowing for adaptive and dynamic program flow control.

Question 2: What are the key challenges in implementing neural programming in real-world applications? Answer: Challenges may include training neural networks effectively, handling complex environmental inputs, and ensuring robust decision-making processes.

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on computer storage media, for neural programming. one of the methods includes processing a current neural network input using a core recurrent neural network to generate a neural network output; determining, from the neural network output, whether or not to end a currently invoked program and to return to a calling program from the set of programs; determining, from the neural network output, a next program to be called; determining, from the neural network output, contents of arguments to the next program to be called; receiving a representation of a current state of the environment; and generating a next neural network input from an embedding for the next program to be called and the representation of the current state of the environment.