Methods and apparatus to provide machine programmed creative support to a user

1. An apparatus comprising:

an artificial intelligence architecture to be trained based on previous inputs of a user;

a processor including:

a first computer core to:

implement a first neural network based on the trained artificial intelligence architecture; and

predict a first action based on a current state of a computer program using the first neural network; and

a second computer core to:

implement a second neural network based on the trained artificial intelligence architecture; and

predict a second action based on the current state of the computer program using the second neural network; and

a controller to select a state based on the action that results in a state that is more divergent from the current state of the computer program.

2. The apparatus of claim 1 , wherein the current state includes at least one of (A) items currently being rendered by the computer program or (B) a last input of the user.

3. The apparatus of claim 1 , wherein the artificial intelligence architecture is trained to predict the next action of the user based on previous inputs of the user with respect to previous states.

4. The apparatus of claim 1 , wherein the state is a first state, the artificial intelligence architecture including:

a variational autoencoder to:

convert a second state into a latent state;

generate a predicted state based on a predicted action;

a third neural network to:

predict a third action based on the latent state;

update based on a value for the third action; and

when the value for the third action is above a threshold, select the third action as the predicted action; and

a fourth neural network to:

estimate the value for the third action based on the latent state; and

update based on a reward for a previous predicted state based on the second state.

5. The apparatus of claim 1 , further including a comparator to (A) determine a first distance between a first state corresponding to the first action to the current state and (B) determine a second distance between a second state corresponding to the second action to the current state, the more divergent state being the state with the larger distance.

6. The apparatus of claim 1 , wherein the controller is to instruct a user interface to render the selected state to the user.

7. The apparatus of claim 1 , wherein:

the first computer core is to generate a third action based on the selected state using the first neural network;

the second computer core is to generate a fourth action based on the selected state using the second neural network; and

the controller is to instruct a user interface to render a subsequently selected state based on the action that is more divergent from the current state of the computer program.

8. The apparatus of claim 1 , wherein the artificial intelligence architecture is to deploy trained artificial intelligence information to the first and second computer core based on instructions from the controller.

9. The apparatus of claim 8 , wherein the controller is to transmit the instructions to the artificial intelligence architecture in response to at least one of a threshold amount of time or a trigger from the computer program.

10. The apparatus of claim 1 , wherein the artificial intelligence architecture is to train based on user feedback corresponding to the selected state.

11. A method comprising:

training, by executing an instruction with a processor, an artificial intelligence architecture based on previous inputs of a user;

implementing, using a first computer core of the processor, a first neural network based on the trained artificial intelligence architecture;

predicting, using the first computer core of the processor, a first action based on a current state of a computer program using the first neural network;

implementing, using a second computer core of the processor, a second neural network based on the trained artificial intelligence architecture;

predicting, using the second computer core of the processor, a second action based on the current state of the computer program using the second neural network; and

selecting, by executing an instruction with the processor, a state based on the action that results in a state that is more divergent from the current state of the computer program.

12. The method of claim 11 , wherein the current state includes at least one of (A) items currently being rendered by the computer program or (B) a last input of the user.

13. The method of claim 11 , wherein the artificial intelligence architecture is trained to predict the next action of the user based on previous inputs of the user with respect to previous states.

14. The method of claim 11 , wherein the state is a first state, further including:

converting a second state into a latent state;

generating a predicted state based on a predicted action;

predicting a third action based on the latent state;

updating based on a value for the third action;

when the value for the third action is above a threshold, selecting the third action as the predicted action;

estimating the value for the third action based on the latent state; and

updating based on a reward for a previous predicted state based on the second state.

15. The method of claim 11 , further including:

determining a first distance between a first state corresponding to the first action to the current state; and

determining a second distance between a second state corresponding to the second action to the current state, the more divergent state being the state with the larger distance.

16. The method of claim 11 , further including instructing a user interface to render the selected state to the user.

17. The method of claim 11 , further including:

generating a third action based on the selected state using the first neural network;

generating a fourth action based on the selected state using the second neural network; and

instructing a user interface to render a subsequently selected state based on the action that is more divergent from the current state of the computer program.

18. The method of claim 11 , further including deploying trained artificial intelligence information to a first and second computer core based on instructions for a controller.

19. The method of claim 18 , further including transmitting the instructions to the artificial intelligence architecture in response to at least one of a threshold amount of time or a trigger from the computer program.

20. The method of claim 11 , further including training the artificial intelligence architecture based on user feedback corresponding to the selected state.

21. A non-transitory computer readable storage medium comprising instructions which, when executed, cause a machine to at least:

train an artificial intelligence architecture based on previous inputs of a user;

use a first computer core to implement a first machine learning model based on the trained artificial intelligence architecture;

use the first computer core to predict a first action based on a current state of a computer program using the first machine learning model;

use a second computer core to implement a second machine learning model based on the trained artificial intelligence architecture;

use the second computer core to predict a second action based on the current state of the computer program using the second machine learning model; and

select a state based on the action that results in a state that is more divergent from the current state of the computer program.

22. The computer readable storage medium of claim 21 , wherein the current state includes at least one of (A) items currently being rendered by the computer program or (B) a last input of the user.

23. The computer readable storage medium of claim 21 , wherein the artificial intelligence architecture is trained to predict the next action of the user based on previous inputs of the user with respect to previous states.

24. The computer readable storage medium of claim 21 , wherein the state is a first state, the instructions to cause the machine to:

convert a second state into a latent state;

generate a predicted state based on a predicted action;

predict a third action based on the latent state;

update based on a value for the third action;

when the value for the third action is above a threshold, select the third action as the predicted action;

estimate the value for the third action based on the latent state; and

update based on a reward for a previous predicted state based on the second state.