TuringTest

Abstract: In the mentifex-class AI Minds, TuringTest is a mind-module serving the purpose of human-computer interaction (HCI).

The TuringTest module serves as a human-computer interface between the AI Mind and one or more human users. Its purpose is to provide avenues of communication between man and machine. In the most primitive AI Minds, the keyboard and the screen of a computer are the main interface. The tactile keyboard serves as a substitute for auditory input, and the monitor screen serves as a substitute for voice output -- unless speech synthesis is channeling output through a loudspeaker or a headphone.

Earlier in AiEvolution, the same mind-module was called HCI for Human-Computer Interaction, before the module names were modified to serve as clickable links on the wiki-pages of the AI documentation. Renaming HCI as TuringTest serves the purpose of making users and coders aware of the well-known test for AI functionality named after the AI pioneer Alan Turing.

The SeCurity module calls the TuringTest module as one of potentially myriad operations affecting AI security. Since the TuringTest operation gives outside agents access into the AI Mind, the AI and the human user are mutually vulnerable to malicious intentions during the operation of the TuringTest. In MindForth and the German Wotan AI, the TuringTest module protects against liability by announcing that there is no warranty for the free AI source code. MindForth and Wotan also state the date and time that the AI Mind came to life, for inclusion during TranScript mode and for the purpose of any contest to see which AI Mind installation is the oldest or has been running the longest. MindForth and Wotan may display instructions for the user on-screen, while the JavaScript AiMind and Dushka programs present checkboxes for the user to click or unclick for a choice of display modes.

Since the JavaScript AI Minds in English and in Russian are flashier and more graphical than the bare-bones robot AI of MindForth and Wotan, there is more leeway for improvisation and razzle-dazzle effects in the JavaScript tutorial programs. Ambitious AI coders in any programming language have the opportunity and the challenge of graphically depicting even the most subtle of mental phenomena occurring in the artificial intelligence, such as the branching filaments of spreading activation and the volatile surfacing of concepts and ideas in the artificial ConSciousness.

The visibly operating TuringTest interface module is somewhat easier to troubleshoot and debug than the more hidden majority of AI mind-modules, because any glitch or software error will tend to show up immediately. Typical problems may involve timing where the rsvp variable is counting down too quickly if the host computer has an extremely fast central processing unity (CPU). The AI coder or installation supervisor may have to adjust the pertinent values.

More subtle problems may arise in connection with the happenstance timing of when a human user begins entering input into the AI Mind or how fast or how slow a user tries to communicate across the keyboard. Once again, the AI coder-in-charge may need to tweak some values not only in the TuringTest module but possibly in other modules involving input and output.

mar8dkpj

The DeKi Programming Journal (DKPJ) is both a tool in coding German Wotan open-source artificial intelligence (AI) and an archival record of the history of how the German Supercomputer AI evolved over time.

Wed.6.MAR.2013 -- Problems with the WhatBe Module

As we implement InFerence in the Wotan German Supercomputer AI, the program tends to call the WhatBe module to ask a question about a previously unknown word. When we input to the AI, "eva ist eine frau", first Wotan makes an inference about Eva and asks if Eva has a child. Then the AI mistakenly says, "WAS IRRTUM EVA" when the correct output should be "WAS IST EVA". This problem affords us an opportunity to improve the German performance of the WhatBe module which came into the German AI from the English MindForth AI.

First we need to determine which location in the AI source code is calling the WhatBe mind-module, and so we insert some diagnostics. Knowing where the call comes from, lets us work on the proper preparation of parameters from outside WhatBe to be used inside WhatBe.

Thurs.7.MAR.2013 -- Dealing with Number in German

We are learning that we must handle grammatical number much differently in the German AI than in the English AI. English generally uses the ending "-s" to indicate plural number, but in German there is no one such simple clue. In German we have a plethora of clues about number, and we can use the OutBuffer to work with some of them, such as "-heit" indicating singular and "-heiten" indicating plural. In German we can also establish priority among rules, such as letting an "-e" ending in the OutBuffer suggest a plural noun, while letting the discovery of a singular verb overrule the suggestion that a noun is in the plural. The main point here is that in German we must get away from the simplistic English rules about number.

Fri.8.MAR.2013 -- Removing Obsolete Influences

In NewConcept let us try changing the default expectation of number for a new noun from plural to singular. At first we notice no problem with a default singular. Then we notice that the InFerence module is using a default plural ("2") for the subject-noun of the silent inference. We tentatively change the default to singular ("1") until we can devise a more robust determinant of number in InFerence.

We are having a problem with the "ocn" variable for "old concept number". Just as with the obsolete "recnum", there is no reason any more to use the "ocn" variable, so we comment out some code.