Computer implemented method for the automated analysis or use of data

1. A computer implemented method for the automated analysis or use of data, comprising the steps of:

(a) storing in a non-transitory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language; in which the structured, machine-readable representation of data includes one or more tenets defining rules, the one or more tenets including at least one constraint tenet, the at least one constraint tenet preventing a bad action;

(b) a computer system analysing a potential action to determine whether executing the potential action would comply or not comply with one or more of the tenets defining rules, and determining that the potential action does comply or does not comply with the one or more of the tenets defining rules; and

(c) the computer system generating a reasoned, natural language explanation that the potential action does comply or does not comply with the one or more of the tenets defining rules.

2. The method of claim 1 , in which the rules include ethics rules.

3. The method of claim 1 , in which at least some of the rules define affirmative or allowed actions or content.

4. The method of claim 1 , in which at least some of the rules define goals, such as optimisable actions or states.

5. The method of claim 1 , in which the rules include web site or chatbot rules, such as abusive content rules.

6. The method of claim 1 , in which the computer system generates a reasoned, natural language explanation that the potential action does comply or does not comply with the at least one constraint tenet.

7. The method of claim 1 in which the structured, machine-readable representation of data that conforms to the machine-readable language comprises semantic nodes and passages; and in which a semantic node represents an entity and is itself represented by an identifier; and a passage is either (i) a semantic node or (ii) a combination of semantic nodes; and where machine-readable meaning comes from the choice of semantic nodes and the way they are combined and ordered as passages.

8. The method of claim 1 , in which the computer system does not change the tenets.

9. The method of claim 8 in which the computer system not changing the tenets comprises the tenets including a tenet prohibiting actions which might result in changes to the tenets.

10. The method of claim 1 , in which the computer system independently checks each potential action against the tenets and discards the potential action if the independent check finds that the potential action is incompatible with the tenets.

11. The method of claim 1 in which the potential action includes changing the state of a device linked to the computer system via a network.

12. The method of claim 1 in which the potential action includes initiating a spoken interaction with a human being.

13. The method of claim 1 in which the computer system learns from one or more of: (i) communication with at least one user and (ii) one external sensor connected to the computer system via a network.

14. The method of claim 1 in which the tenets are at least partially represented by combinations of identifiers and where at least some of the identifiers represent concepts corresponding to real-world things.

15. The method of claim 1 in which the non-transitory computer-readable medium comprises a long-term non-transitory computer-readable medium; and in which the computer system comprises a short-term memory; the long-term non-transitory computer-readable medium including a tenet-store containing machine-readable tenets representing rules to guide the computer system and in which the computer system receives events and utilises the events, the contents of the long-term non-transitory computer-readable medium, the contents of the short-term memory and the tenets to do actions that conform with the tenets.

16. The method of claim 15 in which the events include communication from at least one user and where the actions include communication to at least one user.

17. The method of claim 15 in which the computer system learns, and stores what it has learned to the long-term non-transitory computer-readable medium.

18. The method of claim 1 in which the computer system comprises a component which generates candidate actions; a component that decides whether to execute the candidate actions with reference to the tenets and a component which executes actions.

19. The method of claim 1 in which the computer system actively excludes knowledge on itself from being used in determining actions.

20. The method of claim 1 in which potential actions are autonomously generated by the computer system.

21. The method of claim 1 in which potential actions are autonomously generated by the computer system according to one or more of the following: as outputs from processing inputs, such as audio or text; as a process that operates substantially continuously; as outputs without any external trigger event to initiate processing or user instruction or action to initiate processing.

22. The method of claim 20 in which the potential actions are automatically executed if they optimize or otherwise positively affect the achievement or realization of the tenets.

23. The method of claim 1 in which reasoning with reference to the machine-readable representation produces further, new machine-readable representations.

24. The method of claim 1 in which generating responses to queries comprises three operations, namely matching with machine-readable representations, such as passages, in a store, fetching and execution of computation units and fetching reasoning passages.

25. The method of claim 1 in which a question is represented in a memory as a machine representation, and the representation of the question, the machine representations previously stored in the non-transitory memory computer-readable medium, computation units and reasoning passages are all represented in substantially the same machine readable language.

26. The method of claim 1 in which reasoning is where machine-readable language is generated from other machine-readable language using reasoning steps that are represented as machine representations, such as passages, which represent semantics of the reasoning steps.

27. The method of claim 1 , in which new information is represented in the structured machine-readable representation, such as semantic nodes or passages, and is stored and used to improve reasoning steps.

28. The method of claim 1 , in which learning takes place from reasoning, in which machine representations that are generated from a chain of reasoning steps, are combined with reasoning passages, and are stored and utilised.

29. Computer-based system configured to analyse data, the computer-based system configured to:

(a) store in a non-transitory memory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language; in which the structured machine-readable representation of data includes one or more tenets defining rules, the one or more tenets including at least one constraint tenet, the at least one constraint tenet preventing a bad action;

(b) analyse a potential action to determine whether executing the potential action would comply or not comply with one or more of the tenets defining rules, and determining that the potential action does comply or does not comply with the one or more of the tenets defining rules; and

(c) automatically generate a reasoned, natural language explanation that the potential action does comply or does not comply with the one or more of the tenets defining rules.

30. The system of claim 29 , in which the computer system is configured to generate a reasoned, natural language explanation that the potential action does comply or does not comply with the at least one constraint tenet.