SYSTEM AND METHOD FOR ARTIFICIAL INTELLIGENCE CLEANING TRANSFORM

1 . A method comprising:

receiving data at a computer system, wherein the data has a plurality of rows;

receiving, from a user at the computer system, a description of a task associated with the data;

receiving, from the user at the computer system, a plurality of example transformations;

receiving, from the user at the computer system, input and output labels;

combining, via at least one processor of the computer system, the task description together with the plurality of example transformations and input and output labels, resulting in a prompt;

executing, via the at least one processor, a machine learning model, wherein the prompt is an input to the machine learning model, and wherein output of the machine learning model comprises an algorithm for executing the task; and

executing, via the at least one processor, the task on the data using the algorithm.

2 . The method of claim 1 , wherein the plurality of example transformations comprise:

an input for a transformation; and

an output for the transformation.

3 . The method of claim 1 , wherein the plurality of example transformations number three.

4 . The method of claim 1 , wherein the description of the task is prose.

5 . The method of claim 4 , further comprising:

executing, via the at least one processor, natural language processing (NLP) on the description of the task, resulting in parsed text,

wherein the prompt further comprises the parsed text.

6 . The method of claim 1 , further comprising:

receiving, at the computer system, feedback regarding accuracy of the execution of the task on the data using the algorithm; and

retraining, via the at least one processor, the machine learning model using the feedback.

7 . The method of claim 1 , wherein the machine learning model is a GPT-3 (Generative Pre-trained Transformer 3) model.

8 . A system comprising:

at least one processor; and

a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

receiving data, wherein the data has a plurality of rows;

receiving a description of a task associated with the data;

receiving a plurality of example transformations;

receiving input and output labels;

combining the task description together with the plurality of example transformations and input and output labels, resulting in a prompt;

executing a machine learning model, wherein the prompt is an input to the machine learning model, and wherein output of the machine learning model comprises an algorithm for executing the task; and

executing the task on the data using the algorithm.

9 . The system of claim 8 , wherein the plurality of example transformations comprise:

an input for a transformation; and

an output for the transformation.

10 . The system of claim 8 , wherein the plurality of example transformations number three.

11 . The system of claim 8 , wherein the description of the task is prose.

12 . The system of claim 11 , the non-transitory computer-readable storage medium having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

executing, via the at least one processor, natural language processing (NLP) on the description of the task, resulting in parsed text,

wherein the prompt further comprises the parsed text.

13 . The system of claim 8 , the non-transitory computer-readable storage medium having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

receiving feedback regarding accuracy of the execution of the task on the data using the algorithm; and

retraining the machine learning model using the feedback.

14 . The system of claim 8 , wherein the machine learning model is a GPT-3 (Generative Pre-trained Transformer 3) model.

15 . A non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause the at least one processor to perform operations comprising:

receiving data, wherein the data has a plurality of rows;

receiving a description of a task associated with the data;

receiving a plurality of example transformations;

receiving input and output labels;

combining the task description together with the plurality of example transformations and input and output labels, resulting in a prompt;

executing a machine learning model, wherein the prompt is an input to the machine learning model, and wherein output of the machine learning model comprises an algorithm for executing the task; and

executing the task on the data using the algorithm.

16 . The non-transitory computer-readable storage medium of claim 15 , wherein the plurality of example transformations comprise:

an input for a transformation; and

an output for the transformation.

17 . The non-transitory computer-readable storage medium of claim 15 , wherein the plurality of example transformations number three.

18 . The non-transitory computer-readable storage medium of claim 15 , wherein the description of the task is prose.

19 . The non-transitory computer-readable storage medium of claim 18 , having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

executing, via the at least one processor, natural language processing (NLP) on the description of the task, resulting in parsed text,

wherein the prompt further comprises the parsed text.

20 . The non-transitory computer-readable storage medium of claim 15 , having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

receiving feedback regarding accuracy of the execution of the task on the data using the algorithm; and

retraining the machine learning model using the feedback.