- A process by which an algorythm can produce other algorhythms, code, and even CAD based off of training data, and training
- many methods each with their own advantages
- One near essential application is complex problems such as autonomous navigation, problem solving, object recognition, etc
- Being software, there are many open source tools
- Main requirements are AI Training Data
- Can create solutions to problems humans could never make
- Can sometimes be hard to explain the core of the "method" it comes up with
- See the CPG Grey Video On It
- Can be a buzzword so keep that in mind when reading headlines etc
- NEED EXPERT EXPLAINING
AI Machine Learning with Deep Neural Networks have been used to produce many types of content that may be valuable to Open Source Ecology.
In 2022, ChatGPT was publicly released. It can produce large quantities of text from a prompt.
Images generated by these tools could be very useful in creating visuals in documentation.
In 2021 Microsoft released GitHub Copilot, which -- using a neural network trained by open-source software on GitHub -- is able to produce code from a prompt describing a function.
TODO: see if anyone has trained a neural network with large quantities of CAD data (eg everything on theingiverse) to produce CAD diagrams (or at least OpenSCAD code) from a prompt
- YouTube: CAD Model Recognition by Deep Neural Network in Solidworks
- IEEE: Using Neural Networks to design CAD of microwave circuits
- 3D Net: classification neural network trained with STL files from thingiverse
- Evolved Antennae
- Shogun (Software)
- Oryx 2
- Eclipse Deeplearning4j
- Nervana Neo