ADVANCED MACHINE LEARNING INTERFACES

1 . A computer-implemented method comprising:

receiving, by a computer system, a technical assistance request from a user;

analyzing, by a translator, the technical assistance request to determine an intent of the user, wherein the technical assistance request comprises a request to generate computer code;

generating or modifying, by the translator, computer code responsive to the intent of the user.

2 . The computer-implemented method of claim 1 further comprising:

translating by the translator the computer code to a high-level representation;

receiving edits to the high-level representation;

generating updated computer code based on the edited high-level representations.

3 . The computer-implemented method of claim 2 , wherein the high-level representation comprises natural language text.

4 . The computer-implemented method of claim 1 further comprising:

receiving the technical assistance request in the form of natural language text;

parsing, by a parser, the technical assistance request into tokens; and

determining the intent of the user from the parsed technical assistance request using a machine learning model.

5 . The computer-implemented method of claim 4 , wherein the machine learning model is a neural network.

6 . The computer-implemented method of claim 1 , further comprising displaying a user interface element comprising a graphical user interface (GUI) request builder, wherein the GUI request builder includes a plurality of visible interface elements for building the technical assistance request.

7 . The computer-implemented method of claim 1 further comprising:

receiving the technical assistance request in the form of an image;

analyzing, by an image analysis program, the technical assistance request and extracting one or more features;

determining the intent of the user from the analyzed technical assistance request using a machine learning model; and

wherein the computer code implements at least a portion of a graphical user interface.

8 . The computer-implemented method of claim 1 further comprising:

receiving the technical assistance request in the form of natural language text;

parsing, by a parser, the technical assistance request into tokens;

analyzing the parsed technical assistance request with a machine learning model and detecting a need for additional information;

generating an information request;

displaying the information request to the user;

receiving additional information from the user;

analyzing the additional information; and

determining the intent of the user from the parsed technical assistance request and the additional information using the machine learning model.

9 . The computer-implemented method of claim 1 , further comprising executing the computer code.

10 . The computer-implemented method of claim 1 , further comprising:

wherein the technical assistance request is a request to query a database; and

automatically generating a sequence of queries to access the database according to the technical assistance request.

11 . The computer-implemented method of claim 1 , further comprising:

wherein the technical assistance request comprises a request to perform a prediction task;

automatically generating a sequence of queries to access a database according to the technical assistance request;

executing the sequence of queries to retrieve data from the database;

automatically performing the prediction task.

12 . The computer-implemented method of claim 1 , further comprising:

wherein the technical assistance request comprises a request to generate markup language source code from a design mock up;

analyzing the design mock up using a machine learning model; and

generating by the machine learning model the markup language source code for the design mock up.

13 . A computer-implemented method comprising:

monitoring, by a computer system, user actions;

analyzing the user actions using a machine learning model;

determining, by the machine learning model based on the user actions, that the user is in need of technical assistance;

determining that the technical assistance needed comprises generating computer code;

generating or modifying, by a translator, computer code responsive to the type of technical assistance needed by the user.

14 . The computer-implemented method of claim 13 , further comprising displaying a message to the user to ask if technical assistance is needed.

15 . The computer-implemented method of claim 14 , further comprising prompting the user for input about the type of technical assistance needed.

16 . The computer-implemented method of claim 13 , further comprising executing the computer code.

17 . The computer-implemented method of claim 13 , further comprising:

translating by the translator the computer code to a high-level representation;

receiving edits to the high-level representation;

generating updated computer code based on the edited high-level representation.

18 . The computer-implemented method of claim 17 , high-level representation comprises natural language text.

19 . The computer-implemented method of claim 13 , further comprising:

receiving the technical assistance request in the form of an image;

analyzing, by an image analysis program, the technical assistance request and extracting one or more features;

determining the intent of the user from the analyzed technical assistance request using a machine learning model; and

wherein the computer code implements at least a portion of a graphical user interface.

20 . The computer-implemented method of claim 13 , further comprising:

wherein the technical assistance request is a request to query a database; and

automatically generating a sequence of queries to access the database according to the technical assistance request.