Deepmind technologies limited (20240177001). NEURAL PROGRAMMING simplified abstract
Contents
NEURAL PROGRAMMING
Organization Name
Inventor(s)
Scott Ellison Reed of Atlanta GA (US)
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.
