Jul 31, 2016

The author proposes a lot of vague ideas in this article (for example "I believe one of the biggest problems is the use of Error Propagation and Gradient Descent") without references or any solid proofs why they are necessary to solve the proposed program (Automate programming using ML?).

In fact there is already a lot of solid work just on this subject:

* Learning algorithms from examples http://arxiv.org/abs/1511.07275 https://arxiv.org/abs/1410.5401

* Generating source code from natural language description http://arxiv.org/abs/1510.07211

* And, the most closest work to what author probably wants, a way to write a program in forth while leaving some functions as neural blackboxes to be learned from examples: http://arxiv.org/abs/1605.06640

* Also there is a whole research program by nothing less than Facebook AI Research that explicitly aims at creating a conversational AI agent that is able to translate user's natural language orders into programs (asking to the user additional questions if necessary): http://arxiv.org/abs/1511.08130 (there is also a summary here http://colinraffel.com/wiki/a_roadmap_towards_machine_intell... )

And deepmind is also working on conversational agents: https://youtu.be/vQXAsdMa_8A?t=1265

Given current success of such models, automating simple programming tasks maybe not as much research as engineering and scaling up problem.

There is a lot of exciting machine learning research out there nowadays. Almost all of this research is available for free from papers posted on arxiv. It is a really good idea to read more about state of the art before coming with new ideas.

Jul 22, 2016

There are signs, if you know were to look for them: http://arxiv.org/abs/1510.07211 https://arxiv.org/abs/1410.4615 http://arxiv.org/abs/1605.06640 https://arxiv.org/abs/1410.5401