View source for Deepmind technologies limited (20240177001). NEURAL PROGRAMMING simplified abstract

=NEURAL PROGRAMMING=


==Organization Name==

[[:Category:deepmind technologies limited|deepmind technologies limited]]


[[Category:deepmind technologies limited]]

==Inventor(s)==

[[:Category:Scott Ellison Reed of Atlanta GA (US)|Scott Ellison Reed of Atlanta GA (US)]][[Category:Scott Ellison Reed of Atlanta GA (US)]]

[[:Category:Joao Ferdinando Gomes De Freitas of London (GB)|Joao Ferdinando Gomes De Freitas of London (GB)]][[Category: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.



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