pmpj0412

Ghost Perl Strong AI cycles through Normal; Transcript; Tutorial; Diagnostic Mode

It is time now in ghost197.pl to show a clean human-computer interface (HCI) and to stop displaying masses of diagnostic messages. Accordingly in the AudInput module we change the user-prompt to say "Tab cycles mode; Esc(ape) quits AI born [date-of-birth]". We insert if-clauses to declare which user input mode is in effect: Normal; Transcript; Tutorial; or Diagnostic. Near the start of ghost197.pl we set the $fyi to a default starting value of unity ("1") so that the human user or Mind-maintainer may press the Tab-key to cycle among user input modes. In AudInput() we insert code to increment $fyi by one point with each press of the Tab-key and to cycle back to unity ("1") for Normal Mode if the user in Diagnostic Mode presses Tab again.

In the MainLoop module we change a line of code to test for $fyi being above a value of two ("2") and, if so, to display the contents of the @psy conceptual array and of the @ear auditory memory array. Thus the user in #3 Tutorial Mode or in #4 Diagnostic Mode will see the storage of current input and current output in the memory arrays. We consider the display of conceptual memory data in Tutorial Mode to be an extremely powerful tool for teaching how the artificial general intelligence (AGI) works. After any input, the user may see immediately how the input goes into memory and how the values in the flag-panel of each row of the @psy array represent the associative tags from concept to concept and from engram to engram.

Next we start commenting out or deleting the display of various diagnostic messages. Over time and over multiple releases of the Ghost AI source code, any AI coder may decide which messages to display in both Tutorial and Diagnostic Modes, or in only one of them. Although we comment out a message involving Russian input, we do not delete the diagnostic message because we may need it when we turn back on Russian as an input language. Russian has become much more important in our Ghost Perl AI because we need Russian or German to demonstrate Machine Translation by Artificial Intelligence. When we have commented out most of the diagnostic messages, we need to put back in some code to show what the user is entering.

pmpj0329

Improving the Ghost Perl AI Human-Computer Interface

The Ghost Perl AI has recently become able to pause its thinking long enough to accept keyboard input from a human user, and to stop waiting either when the user presses the Enter-key, or when there is no input at all, or when the user fails to enter the carriage-return. Now we need to discontinue the pause more quickly when there is no activity from the keyboard, so we will try creating a $gapcon variable to be incremented with each loop expecting but not receiving an input character, and to be reset to zero when there is indeed an input character. It works.

A minor problem in ghost186.pl is that the MainLoop is displaying the contents of the @psy and @aud memory arrays without a gap-line between input and output. The problem seems to lie with the setting of the time $t value. No, the solution was to set the $krt value in the Sensorium() module after the call to AudInput(), so that the MainLoop can separately display memory data before input and then after input, separated by a blank line.

Next in ghost186.pl we tackle the problem where the input diagnostic display was no longer showing each input character prominently left-justified down the edge of the MS-DOS window. We simply moved some old code from an obsolete area up into the currently operative AudInput code. Doing so not only gave us the left-justified display, but we also saw immediately that the $len value is not being reset to zero after each word of input, which prevents words beyond the very first word from being recognzed. We track down and fix the $len problem.

pmpj0325

Ghost183.pl pauses for human input and then continues thinking.

There is a chance that we will attain the Technological Singularity today in our coding of the Ghost Perl Webserver Strong AI, but then we will have to figure out how to blame somebody else for it. Meanwhile we start with a mundane problem. Persons who download Forth and run MindForth see a quivering prompt that invites input from the human user. More importantly, the dynamic, quivering prompt conveys the sense that something is alive and sentient in the AI Forthmind. We need the same user experience in the Ghost Perl AI.

The jittery prompt is achieved in the MindForth AudListen module by having it issue an ASCII SPACE-32 and BACK-SPACE-8 over and over again. When we try to achieve a similar human-computer-interface (HCI) in the Perlmind, it is confusing at first because we seem to be altering multiple lines of the screen simultaneously. Actually we are seeing the AudListen loop re-drawing the screen instantaneously.

Let us shift our attention to the problem of how to insert a default CR carriage-return if there is no human input from AudListen.

From page 354 of the Perl Black Book we have just learned how to use "ord" to deal with ASCII vales as we are so accustomed to do in Forth.

Since we are finding it difficult to detect a "CR" carriage-return in AudListen with ReadKey, we may just let both the AudInput loop and the AudListen loop run their course, with a really long AudInput loop as a way of presenting the human user with an apparent pause in the thinking of the AI.

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.