2. Maestro AI Vision and Design Overview

3. Definitions Maestro: A naïve Sensorimotor Engine prototype. Sensorimotor Engine: Combining sensory and motor functions to produce an encapsulated block of functionality.

4. Maestro AI

5. Environment Here’s a diagram of its proposed functionality.

6. Maestro AI Environment The engine is modeled as separate from the environment.

7. Maestro AI Environment Senses The engine senses each input from the environment as a state of the environment.

8. Maestro AI Environment Senses Motor (behavior) The engine acts upon the environment to produce new states.

9. Maestro AI Environment Senses Motor (behavior) Causal map Through this interaction (Sensorimotor loop) it creates a causal map.

10. Maestro AI Environment Senses Motor (behavior) Causal map For instance: it may sense a new state.

11. Maestro AI Environment Senses Motor (behavior) Causal map The engine chooses an action in response to the stimuli.

12. Maestro AI Environment Senses Motor (behavior) Causal map The environment is perturbed into a new state, which is added to the causal map.

13. Maestro AI Environment Senses Motor (behavior) Causal map Which generates a new behavior in response.

14. Maestro AI Environment Senses Motor (behavior) Causal map This process continues: building an internal representation of the environment.

15. Maestro AI Environment Senses Motor (behavior) Causal map The engine does not know ahead of time if it will encounter a new state…

16. Maestro AI Environment Senses Motor (behavior) Causal map Or one that has been previously seen one.

17. Maestro AI Environment Senses Motor (behavior) Causal map It fills out the map, which is a finite state machine.

18. Maestro AI Environment Senses Motor (behavior) Causal map The more nodes there are, the more transitions are recorded, exponentially.

19. Maestro AI Environment Senses Motor (behavior) Causal map The state of the environment can be represented by raw data or preprocessed data.

20. Maestro AI Environment Senses Motor (behavior) Causal map Preprocessing the data reduces the number of potential states the engine can see.

21. Maestro AI Environment Senses Motor (behavior) Causal map Motor behaviors, too, can be granular or complex.

22. Maestro AI Environment Senses Motor (behavior) Causal map The engine only has a certain number of behaviors it can produce on the smallest scale.

23. Maestro AI Environment Senses Motor (behavior) Causal map Lets assume the Causal Map is now complete. This is a very simple environment.

24. Maestro AI Environment Senses Motor (behavior) Causal map A user may control the engine. User

25. Maestro AI Environment Senses Motor (behavior) Causal map The engine can be used to manage an environment that the user may not understand. ?

26. Maestro AI Environment Senses Motor (behavior) Causal map The user can control the engine by issuing commands or goals matching a state. User

27. Maestro AI Environment Senses Motor (behavior) Causal map Given any input state of the environment the engine may produce any other state. User

28. Maestro AI Environment Senses Motor (behavior) Causal map By using it’s causal map to produce an efficient series of behaviors. User

29. Maestro AI Environment Senses Motor (behavior) Causal map User

30. Maestro AI Environment Senses Motor (behavior) Causal map User

31. Maestro AI Environment Senses Motor (behavior) Causal map User

32. Maestro AI Environment Senses Motor (behavior) Causal map The engine may be able to achieve any state in the environment from any other state. User

33. Maestro AI Static Environment Senses Motor (behavior) Causal map Of course this only works for “static” environments; where the engine is the only actor. User

34. Maestro AI Simple Environment Senses Motor (behavior) Causal map Also, so far the environment must be quite simple with relatively few possible states. User

35. Maestro AI ∞ Environment Senses Motor (behavior) Causal map But what if you had a static environment with an infinite (or many) number of states? User

36. Maestro AI ∞ Environment Senses Motor (behavior) Causal map You couldn’t possibly map each state; let alone, every possible state-to-state transition. User

37. Maestro AI ∞ Environment Senses Motor (behavior) Causal map The engine needs to be able to predict state transitions for behaviors it hasn’t tried yet. User

38. Maestro AI ∞ Environment Senses Motor (behavior) Causal map And prediction requires the detection and extrapolation of patterns in the data. User Patterns

39. Maestro AI ∞ Environment Senses Motor (behavior) Causal map There are patterns in the data because there is a structure to the environment. User Structure Patterns

40. Maestro AI ∞ Environment Senses Motor (behavior) Causal map The engine must infer the rules that govern state transitions; underlying structure. User Structure Patterns Inference map

41. Maestro AI ∞ Environment Senses Motor (behavior) Causal map The inference map should mirror the rules that govern the environment. User Inference map Structure Patterns

42. Maestro AI ∞ Environment Senses Motor (behavior) Causal map Given a current state, the inference map can produce a local theoretical causal map. User Inference map Structure Patterns

43. Maestro AI ∞ Environment Senses Motor (behavior) Causal map And a discovered or observed causal map can inform the inference map. User Inference map Structure Patterns

44. Maestro AI ∞ Environment Senses Motor (behavior) Causal map Even two theoretical causal maps can help inform the inference map. User Inference map Structure Patterns

45. Maestro AI ∞ Environment Senses Motor (behavior) Causal map In short the engine needs to infer why the environment changes the way it does. User Inference map Structure Patterns

46. Maestro AI ∞ Environment Senses Motor (behavior) Causal map User Inference map Structure Patterns Producing the causal map is the trivially easy part of the sensorimotor feedback loop.

47. Maestro AI ∞ Environment Senses Motor (behavior) Causal map Generating and “inference map” or it’s equivalent model is the difficult part. User Inference map Structure Patterns

48. Connecting Multiple Maestros For complex environments Maestros can form a hierarchy to manage the environment.

49. Connecting Multiple Maestros Each engine would receive a portion of the environment state. X: 22.7Y: 90.8 Environment

50. Connecting Multiple Maestros Simplified sensory data travels up the hierarchy. 2090 Environment

51. Connecting Multiple Maestros A user gives a command to achieve a certain high level (simplified) state. User (30, 80) Environment

52. Connecting Multiple Maestros The top engine gives orders for each of the lower engines to produce the desired state. User 3080 Environment

53. Connecting Multiple Maestros The bottom level engines find paths in their database to achieve the desired state. User Action(s) X: +7.3 Action(s) Y: -10.8 Environment

54. Connecting Multiple Maestros The hierarchy of engines allows the system to see big picture and details all at once. User invariant view; higher order concepts Rapidly changing view and detailed motor commands Environment

55. Connecting Multiple Maestros Another way to think of the Hierarchy of engines: a structure of memoizers. Environment User

56. Limitations Not generalized Artificial Intelligence.

57. Limitations Not generalized Artificial Intelligence. Not good at producing its own goals.

58. Limitations Not generalized Artificial Intelligence. Not good at producing its own goals. Not good in environments where things cannot be modeled.

59. Limitations Not generalized Artificial Intelligence. Not good at producing its own goals. Not good in environments where things cannot be modeled. Not good in chaotic environments.

60. Limitations Not generalized Artificial Intelligence. Not good at producing its own goals. Not good in environments where things cannot be modeled. Not good in chaotic environments. Not good to interact with humans.

61. Strengths Good in an static, structured environment.

62. Strengths Good in an static, structured environment. Good with even complicated systems of interdependent relationships if relationships are symmetrical or uniform.

63. Strengths Good in an static, structured environment. Good with even complicated systems of interdependent relationships if relationships are symmetrical or uniform. Computer Programming?

64. Strengths Good in an static, structured environment. Good with even complicated systems of interdependent relationships if relationships are symmetrical or uniform. Computer Programming? Able to command amazing brilliance at repeatable patterns, not able to handle chaotic / indeterminate situations well.

65. Why is this important?

66. There is knowledge available in the world.

67. Why is this important? But all knowledge must be discovered from raw data by somebody.

68. Why is this important? It can then be employed to produce goods, which are essentially packaged knowledge.

69. Why is this important? However, this process involves quite a lot of effort. Labor

70. Why is this important? With a sufficiently powerful Maestro AI intellectual labor can be exported. Labor Goal Labor

71. Why is this important? For the first time in human history humans can export creative labor to machines.

72. Why is this important? Products no longer embody knowledge; knowledge is encapsulated in an active Maestro.

73. Why is this important?