mfpj0724

MindForth Programming Journal (MFPJ)

The MindForth Programming Journal (MFPJ) is both a tool in developing MindForth open-source artificial intelligence (AI) and an archival record of the history of how the AI Forthmind evolved over time.

Thurs.24.JUL.2014 -- MindForth AI moves to a Windows XP development platform.

MindForth came into being in 1998 on the Commodore Amiga 1000 computer as a port from the Amiga Mind.Rexx AI program, written in MVP-Forth from Mountain View Press. Around 1999, MindForth moved to a Windows 98 machine provided by Free-PC.com and to 16-bit FPC-Forth. Around 2001, MindForth moved to a Windows 95 Packard-Bell tower computer and to 32-bit Win32Forth. As the original author of Mind.Rexx and of MindForth, yesterday on 23 July 2014 I downloaded W32FOR42_671.zip onto the same Windows XP Acer Aspire One netbook which I have been using to develop the Russian Dushka AI program in JavaScript for MSIE. I unzipped W32FOR42_671.zip with my own legitimate copy of WinZip, which created a C:\WIN32FOR directory to hold all the decompressed files of Win32Forth. From the Web I downloaded the 24jan13A.F most current source code of MindForth and I saved it into the C:\WIN32FOR directory and as a text-file into a monthly C:\JUL01Y14\MFPJ directory on the Acer netbook.

I was able to get MindForth running on the Windows XP netbook by navigating with the "cd" (change directory) command into the C:\WIN32FOR directory where I typed "win32for.exe" and pressed "Enter"; then "fload 24jan13A.F" and the Enter-key; and finally "MainLoop" followed by the Enter-key. The AI Forthmind began to think its own thoughts on the screen, but the program soon crashed in its new environment, both during interaction with me and when allowed to think without human input. It was not a complete Snow Crash; but just as fatal with a pop-up message announcing "Exception # C0000005" and shutting down Win32Forth upon my clicking "Cancel" on the message. The naive and sentimental Forthcoder is not daunted or dismayed by such an AI-Mind-crash, but welcomes instead the chance to troubleshoot the AI and make it compatible with Windows XP. To debug MindForth, we will create a new version and seed it with diagnostic messages in order to find out just where and why the program is crashing with an "Exception" message. Long familiarity with MindForth causes me to suspect that there is probably a "boundary violation" where the software is trying to index one step beyond the limits of an array. We have noticed recently that searching Google for MindForth yields an auto-complete expansion of the search terms to "mindforth source code" -- an indication that Netizens have been looking for the free AI source code that we are working on right here and now. MindForth has also received a prominent mention at http://aihub.net/artificial-intelligence-lab-projects so we are motivated to make the best AI Mind that we can with MindForth and the other Mentifex AI programs.

Thurs.24.JUL.2014 -- Debugging Windows XP MindForth

In the C:\WIN32FOR directory, we enter win32for.exe to start running Win32Forth. Then we use the "File" drop-down menu and "Edit Forth File..." to click on "24jan13A.F" and "Open" it for editing and saving under a new name. Actually, we will save it immediately as "24jul14A.F" so as not to corrupt the old file by changing anything. First, however, we notice that the bottom of our WinViewX screen tells us that there are 5,173 lines of code with a size of 236,908 characters. Under the "File" drop-down menu we click on "Save File As.." and we enter "24jul14A.F" before clicking the "Save" button. We then close the WinViewX window because we want to test the new file before we proceed. We enter "fload 24jul14A.F" and we get the "ok" prompt which means that the file has successfully loaded into Win32Forth. When we enter "MainLoop" and observe without human input, the AI thinks about two thoughts and then stops with the "Exception # C0000005" pop-up message. This denouement occurs both in the default normal mode and in the Transcript mode that we invoke by pressing the Tab-key. It is time to start inserting diagnostic messages.

In the ThInk module we enter and reformulate a diagnostic message that we find commented-out in another mind-module. We forget to un-comment the code, so at first no diagnostics appear. Then we get the diagnostics, but with no change in program behavior -- it still crashes. But we see the light and we remember the Dao of debugging, that is, you figure out what modules the AI is calling and you insert diagnostics deeper and deeper into the program.

Let's see, the first part of AI thinking is to call the NounPhrase module, so let us diagnosticate NounPhrase. Aha! NounPhrase gives us some (meaningless?) diagnostics just before the Exception-crash, but the ThInk module does not. Therefore, Inspector Clouseau, the problem may lie within NounPhrase or within a module called by NounPhrase. By the way, instead of cluttering up this MFPJ journal entry with the actual diagnostic messages -- unless they become really important -- we can meta-publish the diagnostics simply by commenting them out but retaining them within the "mindforth source code" that we eventually publish on the Web. In that way, any interested party (corporate AI shop? national Ministry of AI? Ph.D. dissertation writer?) can see exactly how we have debugged the AI by inspecting the diagnostic messages that we will leave in for at least one iteration of releasing the code. So now let's plunk some diagnostics down in the VerbPhrase module in order to see if the AI thought processes are making it through NounPhrase and into VerbPhrase before the Exception-crash.

As the Forthmind thinks in English, we are getting diagnostic messages from both NounPhrase and VerbPhrase up until the dying thought of the AI, where NounPhrase reports something but VerbPhrase is silent, both in terms of output and in terms of diagnostics. So the crash could be occurring within the NounPhrase module. Therefore let us insert additional diagnostics towards the end of NounPhrase. We do so, but the software crashes before it reaches the diagnostics at the end of NounPhrase. Next we should try some diagnostics in the middle of NounPhrase. We insert diagnostics after the end of the search for the motjuste, but program-execution does not get that far and instead the Exception-crash occurs. So the problem may lie within the search for motjuste. We insert a diagnostic just before the ELSE-clause in the motjuste-search, and the diagnostic gets executed many times during non-crash thought, but not at all during generation of the thought that eventuates in the Exception-crash.

At the deepest indentation of the motjuste-search, where the "audjuste" variable is loaded with a value, we insert a diagnostic message. We run the AI. Gobsmack! From deepest NounPhrase, we get three diagnostic messages just before the Exception-crash. We notice that there is a "verblock" value of "423" as reported by the diagnostics just before the crash, so we search through the source code for the the number "423". Its only, unique appearance is at time-point t=554 in the EnBoot sequence, where "423" is assigned to the "tqv" (time-quod-vide) variable. But there is no t=423 time-point. It is interstitial, between the words "WHEN" and "WHERE" in the English bootstrap. Let us look at the source code of the JavaScript AI and see what is there. In the 14apr13A version of the JavaScript AI, at t=554 the value of "557" is assigned to "tqv", so "423" is wrong in the MindForth AI. In fact, two of the values in the Forth AI seem to have been erroneously held over from the older Forthminds before the EnBoot concepts received new concept-numbers. Let us change the pertinent section of the MindForth EnBoot to conform to the values in the JavaScript AI EnBoot() module. Hmm, when we correct the EnBoot segment, we get different output, but we still incur the same Exception-crash.

Now after massive diagnostics we find that the Exception-crash is occurring during the search for "motjuste" when the Index is at a value of "542", a point in time. Let us see what is at the t=542 time-point. We do see a t=552 error where "1" is used instead of "!" for storing a value. Let us fix that mistake.

As we correct various legacy errors from older versions of MindForth, the Exception-crash finally moves out of the time series of the EnBoot sequence and occurs once at t=615 in the time-span beyond EnBoot. Since our diagnostic message shows that the Index "I" has a value of "615" when the program crashes, MindForth must be traversing a loop at the t=615 time of the crash.

Thurs.24.JUL.2014 -- Solution found for defective search loop

Since our Exception was crashing the AI when NounPhrase was already supposed to have found a noun or a pronoun, we decided to try inserting an "ELSE LEAVE" statement just before the Forthword "THEN" ending the search-loop. It worked. The AI stopped crashing and began to think interminably. However, our Acer netbook seems to run at a high speed, and so we may need to increase some "rsvp" values at places in the program.

Table of Contents (TOC)

• Thurs.24.JUL.2014 -- MindForth AI moves to a Windows XP development platform.

• Thurs.24.JUL.2014 -- Debugging Windows XP MindForth

• Thurs.24.JUL.2014 -- Solution found for defective search loop

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.

apr6jsai

The JavaScript artificial intelligence (JSAI) is a clientside AiApp whose natural habitat is a desktop computer, a laptop or a smartphone.

1 Wed.3.APR.2013 -- "nounlock" May Not Need Parameters

In the English JSAI (JavaScript artificial intelligence), the "nounlock" variable holds onto the time-point of the direct object or predicate nominative for a specific verb. Since the auditory engram being fetched is already in the proper case, there may not be any need to specify any parameters during the search.

2 Fri.5.APR.2013 -- Orchestrating Flags in NounPhrase

As we run the English JSAI at length without human input and with the inclusion of diagnostic "alert" messages, we discover that the JSAI is sending a positive "dirobj" flag into NounPhrase without checking first for a positive "predflag".

3 Sat.6.APR.2013 -- Abandoning Obsolete Number Code

Yesterday we commented out NounPhrase code which was supposed to "make sure of agreement; 18may2011" but which was doing more harm than good. The code was causing the AI to send the wrong form of the self-concept "701=I" into the SpeechAct module. Now we can comment out our diagnostic "alert" messages and see if the free AI source code is stable enough for an upload to the Web. Yes, it is.

mar16dkpj

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.

1 Thurs.14.MAR.2013 -- Seeking Confirmation of Inference

In the German Wotan artificial intelligence with machine reasoning by inference, the AskUser module converts an otherwise silent inference into a yes-or-no question seeking confirmation of the inference with a yes-answer or refutation of the inference with a no-answer. Prior to confirmation or refutation, the conceptual engrams of the question are a mere proposition for consideration by the human user. When the user enters the answer, the KbRetro module must either establish associative tags from subject to verb to direct object in the case of a yes-answer, or disrupt the same tags with the insertion of a negational concept of "NICHT" for the idea known as "NOT" in English.

2 Fri.15.MAR.2013 -- Setting Parameters Properly

Although the AskUser module is asking the proper question, "HAT EVA EIN KIND" in German for "Does Eva have a child?", the concepts of the question are not being stored properly in the Psi conceptual array.

3 Sat.16.MAR.2013 -- Machine Learnig by Inference

Now we have coordinated the operation of InFerence, AskUser and KbRetro. When we input, "eva ist eine frau" for "Eva is a woman," the German AI makes a silent inference that Eva may perhaps have a child. AskUser outputs the question, "HAT EVA EIN KIND" for "Does Eva have a child?" When we answer "nein" in German for English "no", the KbRetro module adjusts the knowledge base (KB) retroactively by negating the verb "HAT" and the German AI says, "EVA HAT NICHT EIN KIND", or "Eva does not have a child" in English.

mar13dkpj

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.

1 Sat.9.MAR.2013 -- Making Inferences in German

When the German Wotan AI uses the InFerence module to think rationally, the AI Mind creates a silent, conceptual inference and then calls the AskUser module to seek confirmation or refutation of the inference. While generating its output, the AskUser module calls the DeArticle module to insert a definite or indefinite article into the question being asked. The AI has been using the wrong article with "HAT EVA DAS KIND?" when it should be asking, "HAT EVA EIN KIND?" When we tweak the software to switch from the definite article to the indefinite article, the AI gets the gender wrong with "HAT EVA EINE KIND?"

2 Tues.12.MAR.2013 -- A Radical Departure

In the AskUsermodule, to put a German article before the direct object of the query, we may have to move the DeArticle call into the backwards search for the query-object (quobj), so that the gender of the query-object can be found and sent as a parameter into the DeArticle module.

It may seem like a radical departure to call DeArticle from inside the search-loop for a noun, but only one engram of the German noun will be retrieved, and so there should be no problem with inserting a German article at the same time. The necessary parameters are right there at the time-point from which the noun is being retrieved.

3 Wed.13.MAR.2013 -- Preventing False Parameters

When the OldConcept module recognizes a known German noun, normally the "mfn" gender of that noun is detected and stored once again as a fresh conceptual engram for that noun. However, today we have learned that in OldConcept we must store a zero value for the recognition of forms of "EIN" as the German indefinite article, because the word "EIN" has no intrinsic gender and only acquires the gender of its associated noun. When we insert the corrective code into the OldConcept module, finally we witness the German Wotan AI engaging in rational thought by means of inference when we input "eva ist eine frau", or "Eva is a woman." The German AI makes a silent inference about Eva and calls the AskUser module to ask us users, "HAT EVA EIN KIND", which means in English, "Does Eva have a child?" Next we must work on KbRetro to positively confirm or negatively adjust the knowledge base in accordance with the answer to the question.

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.

mar5dkpj

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.

1 Sun.3.MAR.2013 -- Problems with AskUser

In our efforts to implement InFerence in the Wotan German AI, we have gotten the AI to stop asking "HABEN EVA KIND?" but now AskUser is outputting "HAT EVA DIE KIND" as if the German noun "Kind" for "child" were feminine instead of neuter. We should investigate to see if the DeArticle module has a problem.

2 Mon.4.MAR.2013 -- Problems with DeArticle

By the use of a diagnostic message, we have learned that the DeArticle module is finding the accusative plural "DIE" form without regard to what case is required. Now we need to coordinate DeArticle more with the AskUser module, so that when AskUser is seeking a direct object, so will DeArticle. There has already long been a "dirobj" flag, but it is perhaps time to use something more sophisticated, such as "dobcon" or even "acccon" for an accusative "statuscon". After a German preposition like "mit" or "bei" that requires the dative case, we may want to use a flag like "datcon" for a dative "statuscon". So perhaps now we should use "acccon" in preparation for using also "gencon" and "datcon" or maybe even "nomcon" for nominative.

3 Tues.5.MAR.2013 -- Coordinating AskUser and DeArticle

A better "statuscon" for coordinating between AskUser and DeArticle is "dbacon", because it can be used for all four declensional cases in German. When we use "dbacon" and when we make the "LEAVE" statement come immediately after the first instance of selecting an article with the correct "dbacon", we obtain "HAT EVA DAS KIND" as the question from AskUser after the input of "eva ist eine frau". We still need to take gender into account, so we may declare a variable of "mfncon" to coordinate searches for words having the correct gender.

mar2dkpj

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 theGerman Supercomputer AI evolved over time.

1 Sat.2.FEB.2013 -- Improving the AskUser Module

To begin a yes-or-no question in German, a form of the verb has to be generated either by a parameter-search or by VerbGen. We will first try the parameter-search using dba for person and nphrnum for number.

2 Tues.26.FEB.2013 -- Assigning Number to a New Noun

For learning a new noun in German, we need to use the OutBuffer in the process of assigning grammatical number to any new noun. We can use a previous article to suggest the number of a noun, and we may impose a default number which may be overruled first by indications obtained from OutBuffer-analysis and secondly by the continuation with a verb that reveals the number of its subject.

For OutBuffer-analysis, we may impose various rules, such as that a default presumption of singular number may be overruled by certain word-endings such as "-heiten" or "-ungen" which would rather clearly indicate a plural form. We may not so easily presume that endings in "-en" or "-e" indicate a plural, because a singular noun may have such an ending. An ensuing verb is a much better indicator of the perceived number of a noun than the ending of the noun is.

Although we may be tempted to detect the ensuing singular verb "ist" and use it to retroactively establish a noun-number as being singular, it may be simpler to use the OutBuffer to look for singular verbs that end in "-t", such as "ist" or "geht". Likewise, a verb ending in "-n" could indicate a plural subject. So should the default presumption for a German noun be singular or plural?

3 Wed.27.FEB.2013 -- Assigning Plural Number by Default

In both German and English, we should probably make the default presumption be plural for new nouns being learned. Then we have a basic situation to be changed retroactively if a singular verb is detected. So let us examine the NewConcept module to see if we can set a plural value of "2" there on the "num" which will be imposed in the InStantiate module.

When we set a num default of "2" for plural in NewConcept and we run the German AI, the value of "2" shows up for a new noun in both the ".psi" report and the ".de" lexical report. Next we need to work on retroactively changing the default value on the basis of detecting a singular verb.

We have tried various ways to detect the "T" at the end of the input of the verb "IST". In the InStantiate module, we were able to test first for a pov of external input and then for the value of the OutBuffer rightmost "b16" value. Thus we were able to detect the ending "T" on the verb. Immediately we face the problem of how retroactively to change the default number of the subject noun from "2" for plural to "1" for singular.

Changing anything retroactively is no small matter in the Wotan German AI, because other words may have intervened between the alterand subject-noun and the determinant verb. We have previously worked on assigning tqv and seq values retroactively from a direct object back to a verb, so we do have some experience here.

4 Thurs.28.FEB.2013 -- Creating the RetroSet Module

Today we will try to create a RetroSet mind-module for retroactively setting parameters like the number of a new subject-noun which has been revealed to be singular in number because it was followed by a singular verb-form, such as "IST" or "HAT" in German. First we must figure out where to place the RetroSet module in the grand scheme of a Forth AI program. Since the "T" at the end of a German verb is discovered in the InStantiate module, we could either call RetroSet from InStantiate, or use a "statuscon" variable to set a flag that will call RetroSet from higher up in the Wotan AI program. Let us create a "numcon" flag that can be set to call Retroset and then immediately be reset to zero. Since InStantiate is called from the DeParser module, we should perhaps let DeParser call RetroSet.

Now we have stubbed in the RetroSet AI mind-module just before the DeParser mind-module in the Wotan German artificial intelligence. RetroSet diagnostically displays the positive value of the numcon flag and then resets the flag to zero. In future coding, we will use the numcon flag not only to call RetroSet but also to change the default value of "2" for plural to "1" for singular in the case of a new German noun that the Wotan AI is learning for the first time.

5 Fri.1.MAR.2013 -- Implementing RetroSet in the German AI

In the German Wotan potentially superintelligent AI, the AudListen module sets time-of-seqneed ("tsn") as a time-point for searches covering only current input from the keyboard into the AI Mind. In the new RetroSet module, we may use "tsn" as a parameter to restrict a search for a subject-noun to only the most recent input to the AI. However, "tsn" is apparently being reset for each new word of input, so we switch to using time-of-voice ("tov") and we get better results. We input "eva ist eine frau" and RetroSet retroactively changes the default plural on "EVA" from a two to a one for singular. Next we need to troubleshoot why we are not getting a better question from AskUser.

sep27ruai

These notes record the coding of the Russian AI Mind Dushka in JavaScript for Microsoft Internet Explorer (MSIE).

1. Thurs.27.SEP.2012 -- Shortening Test-Range for Verb-Recog

The Dushka Russian artificial intelligence (RuAi) is not properly recognizing a second-person singular verb-form in the ruLexicon Russian lexical array. When we type in the Cyrillic of "Ty veedyeesh menya" for "You see me," the Russian verb is being recorded in the ruLexicon with an erroneous value of "1" for first person instead of "2" for second person.

Apparently the AudListen code for discrimination among grammatical persons was written too specifically for verbs like "dyelayesh" in January of 2012. We may be able to relax the strictness of comparisons by not testing for the vowel just before the personal ending.

We went into the AudListen code for recognizing "delayesh" in the second-person singular and we commented out just the test for the vowel. Then we ran Dushka and immediately the RuAi was able to recognize "Ty veedyeesh menya" properly for "You see me" and the AI answered "Ya veezhoo tebya" for "I see you". This instance was one of the easiest bug-fixes of our Russian AI coding experience. Next we may need to comment out the vowel-tests for the other personal forms of a present-tense Russian verb.

Immediately we wonder if the whole present-tense paradigm will start working properly for most if not all the Russian verb conjugations when we stop testing for the vowel inside the inflectional ending. It also occurs to us that the RuAi may start learning Russian verb-forms regardless of the numbered conjugations thought up by human scholars of philology over the centuries since Greek and Roman times. If we tweak the recognition-code that we implemented for one conjugation and it starts to work for all the conjugations, then we may have accidentally bypassed the whole issue of worrying about how to deal with different Russian conjugations.

2. Fri.28.SEP.2012 -- Non-Russian Troubleshooting of ru120926

Working today on an old computer where we may not type in Cyrillic, nevertheless we may use a special ru120926T.html test version of the ru120926.html Russian artificial intelligence (RuAi) to determine why the RuAi suddenly says "OSHEEBKA" ("error") rather early in its operation without human input (and therefore without Cyrillic typing).

The first place to look for the cause of the problem is in the NounPhrase module which erroneously outputs OSHEEBKA instead of a correct direct object.

Well, isn't that situation weird? First we put a diagnostic "alert" message at the start of NounPhrase, and we got nowhere -- nothing of value was revealed. Next we put a diagnostic alert in NounPhrase where there was a chance for "subjectflag" to change from its default value of one ("1") to a zero in the presence of either a direct object or a predicate nominative. Still nothing special was revealed. We finally got results when we inserted a conditional alert message to tell us what "motjuste" had been chosen in the condition of looking for a non-subject. The RuAi told us that it had selected concept number "704" just before erroneously outputting the "OSHEEBKA" error message. We recognized "704" as having to be a personal pronoun, but which one? It used to be the accusative case "MENYA" of the Russian pronoun number 701 "YA" for English "I". We no longer use number "704" as a separate concept, because "701" takes care of all forms of "YA" under the influence of the "dba" parameter for the grammatical case involved. The number "704" only shows up in obsolete code that we need to remove from the Russian AI.

When we comment out some legacy NounPhrase code that was invoking the concept number "704", the RuAi stops saying "OSHEEBKA" and declares that the motjuste is concept number "701" or the Russian word "YA" in the nominative for English "I". This result is not satisfactory. There should perhaps have been a "nounlock" after the verb "PONIMAYU". We may have to get rid of the "audme" variable not only in the Russian AI but also in the Forth and JavaScript English AI Minds, then find a form of "ME" through a search based on parameters.

aug23ruai

These notes record the coding of the Russian AI Mind Dushka in JavaScript for Microsoft Internet Explorer (MSIE).

1. Thurs.23.AUG.2012 -- Diagnosing Selection of Subjects

As we troubleshoot the Dushka Russian AI in JavaScript for Microsoft Internet Explorer (MSIE), probably the first point of departure must be inserting a diagnostic "alert" message to let us know how the NounPhrase module is selecting the subject for a sentence of thought in Russian. No matter how a subject is chosen, we want the verblock mechanism to force the retrieval of a particular verb from the so-called IdeaPlex.

Our first major problem after some human input is that NounPhrase selects as most activated a potential subject of "tebya" with a carried-over, spurious "verblock" that does not even lead to a verb, but rather to "tebya" itself. We have probably solved this problem already in the English JSAI.

By searching downwards for "tqv" (the source of "verblock") simultaneously in the Russian JSAI and the English JSAI, we discover that in the English JSAI on 15aug2012 we inserted into InStantiate a line of code to prevent spurious carry-overs of the "tqv" value when "seq" is at zero. Now we insert the same code into the Russian JSAI. Running the AI, we do not get an improvement. Then at the start of WhoBe we also put a zeroing out of "tqv" taken from the English JSAI. Still there is no improvement. In the Russian AI, we then intoduce into WhatBe the same tqv-zeroing as was done in the English JSAI. Again there is no improvement.

2. Fri.24.AUG.2012 -- Affecting Activation of Subjects

It may be necessary to implement code that will switch from an oblique case of an activated concept and find nominative nodes to serve as the subjects of incipient thoughts.

If a direct object is left activated at the end of a sentence, all the nodes of that concept should receive a blanket activation through OldConcept or NounAct. Then NounPhrase may choose nominative nodes as candidates for the subject of a sentence. (Maybe we should make nominative nodes receive a higher activation.) So the process of having a residually activated concept switch from being a direct object in an old thought to being a subject in a new thought should work by whatever mechanism puts a blanket activation on all the nodes of a concept.

3. Tues.28.AUG.2012 -- Finding "verblock" Verb-forms

We need to put in some diagnostic messages and see what residual activation occurs for a direct object.

Today in the VerbPhrase module we are building up some code which, in the presence of a positive verblock, will still go to the "verblock" time-point in the Ru-array but will not automatically accept the verb-form originally deposited there, typically during human input. Instead, the new code conducts a search of the ruLexicon to find a verb-form with the correct number and person. Initially we forgot to search for the concept-number, so we accidentally got the correct ending but on the wrong verb.

4. Wed.29.AUG.2012 -- VerbGen Returns Inveniend Verb-stem

Yesterday we made some major progress in getting the RuAi to search for correct Russian verb forms, but the new code was not yet perfect, so today we need to make improvements. However, we should probably save and archive yesterday's version so that we can recover from any unforeseen errors.

Now there is a problem because the new, integrated search-code is finding the correct archival verb-form, if it is available, but the verb is appearing in duplicate. Apparently the rest of the VerbPhrase code is finding a "vphraud" recall-vector all over again. We should be able to thwart that phenomenon.

As we start to prepare some documentation of the AudBuffer, OutBuffer and VerbGen modules, we notice that our Russian AI code needs to make use of pertinent variables such as the "gencon" status flag and the "audbase" recall-vector to identify the verb whose inflectional ending must be changed. As soon as we use "audbase" in our code, the Russian AI stops switching to a different verb and at least outputs the stem of the verb that we are trying to change. Since we have also set the "gencon" flag, VerbPhrase calls VerbGen but does not make its normal main call to SpeechAct, so we do not get an extra verb-form as output.

5. Thurs.30.AUG.2012 -- VerbGen Needs "dba" Parameter

Yesterday VerbGen was returning only the stem of an inveniend verb and not the inflected personal ending. However, delivering the stem was a major improvement in the Russian AI functionality. Today we found that we needed only to set the "dba" parameter properly before calling VerbGen, and the Russian AI was able to provide a correct form of the required verb.

jul06mfpj

MindForth Programming Journal


1 Fri.6.JUL.2012 -- Debugging after Major Code Revision

In the MindForth artificial intelligence (AI) we are now letting the AI run in tutorial mode without human input in order to troubleshoot any glitches that occur after the major changes of the most recent release. Without human intervention and under the influence of the KbTraversal module, the AI calls various subroutines to prompt a dialog with any nearby human. We observe some glitches that are due perhaps to a lack of proper parameters when a subroutine is called. We intend to debug the calling of the various subroutines so that we may display an AI Mind that thinks rationally not only when left to its own devices but also when the AI must think in response to queries or comments from human users.

2 Sat.7.JUL.2012 -- Solving a Problem with WhatAuxSDo

In the course of letting MindForth run without human input, we noticed that eventually the WhatAuxSDo module was called for the subject of concept #56 "YOU" and the AI erroneously asked "WHAT DO ERROR DO". By inserting a diagnostic message, we learned that WhatAuxSDo was not finding a "subjnum" value for the #56 "YOU" concept and thus could not find the word "YOU" in a search of the English "En" array. We went into the EnBoot sequence and changed the "num" value for "YOU" from zero ("0") to one ("1"). The AI correctly said, "WHAT DO YOU DO". However, we may need to debug even further and find out why the proper value of "num" for "YOU" is not being set during the output.

3 Sun.8.JUL.2012 -- Tightening Code for Searchability

When we search the free AI source code for "2 en{" which should reveal any storing or retrieval of a "num" value, we do not find any code for storing "num" in the English lexical array. Therefore we should search for "5 en{" to see where the part-of-speech "pos" is stored. We do so, and still we do not find what we need. Then we try searching for "5 en{" with an extra blank space in the search, and we discover that a form of "pos" is stored both in EnVocab and in OldConcept. At the same time we see that "num" is also stored in the same two mind-modules. Now we should be able to troubleshoot the problem and find out why English lexical "num" is not being stored during processes of thought. First however, we will try to tighten up the code so that only one space intervenes for future occasions when we are trying to find instances of array-manipulation code.

4 Wed.11.JUL.2012 -- Num(ber) in the English Lexical Array

We need to discover where elements of the flag-panel are inserted into nodes of the English lexical array, so that the "num(ber)" value may be stored properly as the AI Mind continues to think and to respond to queries from human users.

5 Fri.13.JUL.2012 -- Correcting Fundamental Flaws

Today in the EnBoot English bootstrap module we are making a blanket change by moving the EnVocab calls down to be on the same line of code as the calls to InNativate, so that the "num(ber)" setting will go properly into EnVocab. Our recent troubleshooting has revealed that WhatAuxSDo needs to find a "num" value in the English lexical array in order to function properly.

6 Sat.14.JUL.2012 -- Tracking num(ber) Values
Next we need to zero in on how the AI assigns "num(ber)" tags during the recognition of words. In OldConcept, it may be necessary to store a default, such as "num" or "unk" and then to test for any positive "ocn" that will simply override the default.

Since we rely on OldConcept to store the number tag, we may need to track where the number-value comes from. AudInput has some sophisticated code which tentatively assigns a plural number when the character "S" is encountered as the last letter in a word. In the work of 4nov2011 we started assigning zero as a default number for the sake of the EnArticle module, but we may need to change the AudInput module back to assigning one ("1") as the default number.

7 Mon.16.JUL.2012 -- Avoiding Unwarrented Number Values

If the most recent "num(ber)" of a word like "ROBOTS" is found to be "2" for plural, we do not want the AI to make the false assumption that the "num(ber)" of the "ROBOTS" concept is inherently plural. Yet we want words like "PEOPLE" or "CHILDREN" to be recognized as being plural.

8 Tues.17.JUL.2012 -- Making Sure of Lexical Number

We may need to go into the NounPhrase subject-selection process and capture the num(ber) value of the lexical item being re-activated within the English lexical array.

jun29mfpj

MindForth Programming Journal

1 Fri.29.JUN.2012 -- IdeaPlex: Sum of all Ideas

The sum of all ideas in a mind can be thought of as the
IdeaPlex. These ideas are expressed in human language
and are subject to modification or revision in the course of
sensory engagement with the world at large.

The knowledge base (KB) in an AiMind is a subset of the IdeaPlex.
Whereas the IdeaPlex is the sum totality of all the engrams of
thought stored in the AI, the knowledge base is the distilled
body of knowledge which can be expanded by means of inference
with machine reasoning or extracted as responses to input-queries.

The job of a human programmer working as an AI mind-tender is to
maintain the logical integrity of the machine IdeaPlex and therefore
of the AI knowledge base. If the AI Mind is implanted in a humanoid
robot, or is merely resident on a computer, it is the work of a
roboticist to maintain the pathways of sensory input/output and the
mechanisms of the robot motorium. The roboticist is concerned with
hardware, and the mind-tender is concerned with the software of the
IdeaPlex.

Whether the mind-tender is a software engineer or a hacker hired
off the streets, the tender must monitor the current chain of thought
in the machine intelligence and adjust the mental parameters of the
AI so that all thinking is logical and rational, with no derailments
of ideation into nonsense statements or absurdities of fallacy.

Evolution occurs narrowly and controllably in one artilect installation
as the mind-tenders iron out bugs in the AI software and introduce algorithmic
improvements. AI evolution explodes globally and uncontrollably when
survival of the fittest AI Minds leads to a Technological Singularity.

2 Fri.29.JUN.2012 -- Perfecting the IdeaPlex

We may implement our new idea of faultlessizing the IdeaPlex by
working on the mechanics of responding to an input-query such as
"What do bears eat?" We envision the process as follows. The AI
imparts extra activation to the verb "eat" from the query, perhaps
first in the InStantiate module, but more definitely in the
ReActivate module, which should be calling the SpreadAct module
to send activation backwards to subjects and forwards to objects.
Meanwhile, if not already, the query-input of the noun "bears"
should be re-activating the concept of "bears" with only a normal
activation. Ideas stored with the "triple" of "bears eat (whatever)"
should then be ready for sentence-generation in response to the query.
Neural inhibition should permit the generation of multiple responses,
if they are available in the knowledge base.

During response-generation, we expect the subject-noun to use the
verblock to lock onto its associated verb, which shall then use
nounlock to lock onto the associated object. Thus the sentence is
retrieved intact. (It may be necessary to create more "lock" variables
for various parts of speech.)

We should perhaps use an input query of "What do kids make?", because
MindForth already has the idea that "Kids make robots".

3 Sat.30.JUN.2012 -- Improving the SpreadAct Module

In our tentative coding, we need now to insert diagnostic messages
that will announce each step being taken in the receipt and response
to an input-query.

We discover some confusion taking place in the SpreadAct module,
where "pre @ 0 > IF" serves as the test for performing
a transfer of activation backwards to a "pre" concept. However,
the "pre" item was replaced at one time with "prepsi", so apparently
the backwards activation code is not being operated. We may need
to test for a positive "prepsi" instead of a positive "pre".

We go into the local, pre-upload version of the Google Code MindForth
"var" (variable) wiki-page and we add a description for "prepsi",
since we are just now conducting serious business with the variable.
Then in the MindForth SpreadAct module we switch from testing in vain
for a positive "pre" value to testing for a positive "prepsi".
Immediately our diagnostic messages indicate that, during generation
of "KIDS MAKE ROBOTS" as a response, activation is passed backwards
from the verb "MAKE" to the subject-noun "KIDS". However, SpreadAct
does not seem to go into operation until the response is generated.
We may need to have SpreadAct operate during the input of a verb
as part of a query, in a chain were ReActivate calls SpreadAct to
flush out potential subject-nouns by retro-activating them.

4 Sat.30.JUN.2012 -- Approaching the "seqneed" Problem

As we search back through versions of MindForth AI, we see that
the 13 October 2010 MFPJ document describes our decision to stop
having ReActivate call SpreadAct. Now we want to reinstate the calls,
because we want to send activation backwards from heavily activated
verbs to their subjects. Apparently the .psi position of the "seqpsi"
has changed from position six to position seven, so we must change the
ReActivate code accordingly. We make the change, and we observe that
the input of "What do kids make?" causes the .psi line at time-point
number 449 to show an increase in activation from 35 to 36 on the
#72 KIDS concept. There is such a small increase from SpreadAct
because SpreadAct conservatively imparts only one unit of activation
backwards to the "prepsi" concept. If we have trouble making the
correct subjects be chosen in response to queries, we could increase
the backwards SpreadAct spikelet from one to a higher value.

Next we have a very tricky situation. When we ask, "What do kids make?",
at first we get the correct answer of "Kids make robots." When we ask
the same question again, we erroneously get, "Kids make kids." It used
to be that such a problem was due to incorrect activation-levels,
with the word "KIDS" being so highly activated that it was chosen
erroneously for both subject and direct object. Nowadays we are
starting with a subject-node and using "verblock" and "nounlock"
to go unerringly from a node to its "seq" concept. However, in this
current case we notice that the original input query of "What do kids make?"
is being stored in the Psi array with an unwarranted seq-value of "72"
for "KIDS" after the #73 "MAKE" verb. Such an erroneous setting seems
to be causing the erroneous secondary output of "Kids make kids."
It could be that the "moot" system is not working properly. The "moot"
flag was supposed to prevent tags from being set during input queries.

In the InStantiate module, the "seqneed" code for verbs is causing
the "MAKE" verb to receive an erroneous "seq" of #72 "KIDS".
We may be able to modify the "seqneed" system to not install
a "seq" at the end of an input.

When we increased the amount of time-points for the "seqneed" system
to look backwards from two to eight, the system stopped assigning
the spurious "seq" to the #73 verb "MAKE" at t=496 and instead
assigned it to the #59 verb "DO" at t=486.

5 Sun.1.JUL.2012 -- Solving the "seqneed" Problem

After our coding session yesterday, we realized that the solution
to the "seqneed" problem may lie in constraining the time period
during which InStantiate searches backwards for a verb needing a
"seq" noun. When we set up the "seqneed" mechanism, we rather
naively ordained that the search should try to go all the way back
to the "vault" value, relying on a "LEAVE" statement to abandon
the loop after finding one verb that could take a "seq".

Now we have used a time-of-seqneed "tsn" variable to limit the
backwards searches in the "seqneed" mechanism of the InStantiate
module, and the MindForth AI seems to be functioning better than ever.
Therefore we shall try to clean up our code by removing diagnostics
and upload the latest MindForth AI to the Web.

feb11ruai

Artificial Intelligence in Russian

1. Thurs.9.FEB.2012 -- Unspoken Be-Verbs as a Default

The Russian-speaking artificial intelligence Dushka needs a default BeVerb module that will silently assert itself as the automatic carrier of thought until a non-be-verb takes over from the provisional default. In our coding of a Russian mind, we will assume that any noun or pronoun, beginning a thought in the nominative case, is automatically the subject of a putative BeVerb until proven otherwise. In this way, our cognitive software will prepare for a BeVerb and switch automatically when a non-be-verb occurs.

We should work first on the comprehension of putative be-verbs and second on their generation, so that what we learn in comprehending be-verbs may be used in generating thoughts involving a BeVerb. So we type into the AI a Russian sentence to see if the software can understand it.

Human: душка робот

Robot: ДУШКА ЧТО ДУШКА ТАКОЕ

We said "Dushka is a robot" but the AI responded only, "Dushka -- what is Dushka?" We need to implement a default BeVerb in the comprehension of a sentence that lacks a visible BeVerb.

In the InStantiate module, we can trap for the input of a "c==32" space-bar when the "seqneed" is set to "8" for want of an incoming verb. We may then do something outrageous, but normal for Russian. From InStantiate we may provisionally send into AudMem a space-bar character with an "audpsi" of "800" for the verb БЫТЬ ("to be"), so that the AI is ready to record any noun coming in as a predicate nominative in conjunction with the be-verb. Now, if we implement such an outrageous step, it is possible that our AI memory-banks will become replete with quasi-spurious engrams of infinitive be-verbs that typically do not materialize. It could be that the presence of a spurious be-verb engram will not matter, if the cancellation of the default occurs as soon as some actual verb comes in. Then cancelling the spurious default will involve removing or nullifying any associative tags laid down momentarily during the enactment of the default.

2. Fri.10.FEB.2012 -- Instantiating Imaginary Be-Verbs

In the InStantiate module we will now experiment with code to create in auditory memory a pseudo-engram of a non-existent be-verb after the perception of a nominative noun or pronoun. Since the Russian-speaking mind waits for a predicate nominative, it needs at least an imaginary be-verb as the holder of associative links between subject and predicate nominative.

Now inside InStantiate we have assembled the code that creates a be-verb pseudo-engram in the three memory arrays for "Psi" concepts, Russian words and auditory engrams. The Psi node is automatically creating a "pre" tag that links the pseudo-verb back to its subject. We need to implement code that will finish the intermediation of the unspoken Russian BeVerb between its subject and the predicate nominative. The code must also cancel or uninstall the imaginary BeVerb if a real verb occurs instead of the provisionally expected BeVerb.

3. Sat.11.FEB.2012 -- Integration of Default Be-Verbs

We have the AI pretending that a BeVerb comes in after a nominative subject, and now we need to create the "seq" tag from the subject to the default BeVerb. First in the InStantiate module we insert a line of code declaring that the pseudo-be-verb is indeed a verb with respect to its part of speech, so that the following code will try to reach backwards to the subject engram and install a "seq" tag referring to the now not-so-imaginary BeVerb. We run the Dushka AI and we type in, ты робот -- which is Russian for "You are a robot", but without the be-verb. We are puzzled when Dushka answers, Я ЧТО Я ТАКОЕ ("I -- WHAT AM I?") and that's all she wrote. It may indicate that her concept of self has been activated by the input referring to "you", but she does not seem to have understood the input. We check the diagnostic display, and we see that her concept of self now has a "seq" tag referring right back to herself instead of to the default Russian BeVerb. What went wrong? We look at the JavaScript source code again, and we see that it was not enough to set the part-of-speech as a verb. We go ahead and we set the Psi concept-number to be that of the Russian be-verb. Then we run the Russian AI again with the same input and we sit there in shock when the AI announces to us: Я РОБОТ. Dushka has just said to us, "I AM A ROBOT" in Russian. From the diagnostic display we discover that the same changes that made Dushka able to understand the idea, made her able to think the idea.

feb4ruai

Artificial Intelligence in Russian

Fri.3.FEB.2012 -- Recognizing Inflections

For the Russian-thinking Dushka AI Mind, we have perhaps stumbled upon a way to avoid the hard-coding of noun paradigms and instead to let the Russian AI learn the inflected endings of Russian nouns from its own experience. For example, right now the Russian artificial intelligence (RuAi) fails to recognize the Psi concept #501 БОГ in the following exchange.

Human: я уважаю бога ("I honor God.")
Robot: ТЫ УВАЖАЕШЬ БОГА ("You honor God.")

Robot: ЧТО БОГА ТАКОЕ ("What is God?")

The diagnostic display reveals that the software has almost recognized the word for God.

559. Б 0 * 1 1 0
560. О 0 * 0 1 0
561. Г 0 * 0 1 501
562. А 0 * 0 0 902

Aha! Suddenly it becomes clear that two things are happening. The Psi concept #501 is indeed being recognized at first, but perhaps the provisional-recognition "prc" variable is not being set, and so AudInput calls NewConcept as if the AI were learning a new word instead of recognizing an old word.

Sat.4.FEB.2012 -- Learning Russian Like a Human Child

Now in a very rough way we have trapped for "zad1" in the AudRecog module so as to recognize a noun (БОГА ) with one character of inflection added onto it. Because the noun was indeed recognized, the InStantiate "seqneed" mechanism tagged the noun in the "ruLexicon" with a "dba" of "4" to indicate a direct-object accusative case. In other words, the Russian AI learned a new noun-form as a human child would learn it, that is, from the speech patterns of another speaker of Russian.

feb1ruai

Artificial Intelligence in Russian

Tues.31.JAN.2012 -- Generating and Recognizing Verbs

In our Dushka Russian AI we have the problem that new verb-forms generated on the fly by the VerbGen module are not being recognized and tagged with critical parameters as they settle into auditory memory. However, it looks as though a verb does get recognized if the "audpsi" tags for the verb in auditory memory extend far back enough to cover the stem of the verb. Therefore, instead of devising ways to bypass the operation of ReEntry calling AudMem, calling AudRecog, we should perhaps instead implement a "backfill" of any verb generated in the VerbGen module to let the "audpsi" tags extend back to the last "pho(neme)" of the verb-stem. Then the "provisional recall" mechanism in AudRecog ought to recognize the verb-form generated by the VerbGen module.

We created a "vip" variable to hold the value of "motjuste" when VerbPhrase calls VerbGen and to transfer the known concept-number of the verb, near the end of the stem in VerbGen, into the provisional "prc" variable for AudRecog. In this way, we got the AI internally to recognize and record verb-forms generated internally by the VerbGen module. However, to get the AI to call the correct verb-forms, we had to modify some recent OldConcept code for deciding what "dba" value to store with a lexical item. Now we have a problem with tagging the "dba" of a simple word like МЕНЯ when it comes in.

We can not rely on the form of МЕНЯ to tell us its "dba" because it could be genitive or accusative. We need to extract clues from the incoming sentence in order to assign the proper "dba" during the storage of МЕНЯ.

Wed.1.FEB.2012 -- Tagging Engrams with Parameters

We can perhaps rely on the "seqneed" mechanism of InStantiate to provide the "dba" parameter for a noun or pronoun entering the mind as user input. (Perhaps the "seqneed" variable should change to a "seqseek" variable for greater clarity.) We may be able to strengthen the use of "seqneed" by adding a kind of "pass-over" when a preposition is encountered, so that the software continues to look for a direct-object noun when a preposition-plus-noun combination is detected and skipped.

Where the InStantiate module tests for a "seqneed" of "5" and encounters a satisfying noun or pronoun to become a "seq" for the verb, we make the assumption that the time "t" identifies the temporal location of the noun or pronoun in both the Psi array and the "ruLexicon" array. We insert two lines of code to first "examine" the Russian lexical array and then to substitute a numeric "4" for the "ru4" flag of the "dba" value. Since the noun or pronoun is going to be the "seq" of the verb, that same noun or pronoun warrants a "dba" of "4" as a direct object that should be in the accusative case. However, we may need to make other arrangements if the verb is intransitive and the noun must be in the nominative as a predicate nominative.

jan29ruai

Artificial Intelligence in Russian

1. Sun.29.JAN.2012 -- Verbs Without Direct Objects

Today in the Dushka Russian AI we begin to address a problem that occurs also in our English AI Mind. Sometimes a verb does not need an object, but the AI needlessly says "ОШИБКА" for "ERROR" after the verb. We need to make it possible for a verb to be used by itself, without either a direct object or a predicate nominative. One way to achieve this goal might be to use the jux flag in the Psi conceptual array to set a flag indicating that the particular instance of the verb needs no object.

We have previously used the "jux" flag mainly to indicate the negation of a verb. If we also use "jux" with a special number to indicate that no object is required, we may have a problem when we wish to indicate both that a verb is negated and that it does not need an object, as in English if we were to say, "He does not play."

One way to get double duty out of the "jux" flag might be to continue using it for negation by inserting the English or Russian concept-number for "NOT" as the value in the "jux" slot, but to make the same value negative to indicate that the verb shall both be negated and shall lack an object, as in, "He does not resemble...."

During user input, we could have a default "jux" setting of minus-one ("-1") that would almost always get an override as soon as a noun or pronoun comes in to be the direct object or the predicate nominative. If the user enters a sentence like "He swims daily" without a direct object, the "jux" flag would remain at minus-one and the idea would be archived as not needing a direct object.

2. Sun.29.JAN.2012 -- Using Parameters to Find Objects

While we work further on the problem of verbs without objects, we should implement the use of parameters in object-selection. First we have a problem where the AI assigns activation-levels to a three-word input in ascending order: 23 28 26. These levels cause the problem that the AI turns the direct object into a subject, typically with an erroneous sentence as a result.
In RuParser, let us see what happens when we comment out a line of code that pays attention to the "ordo" word-ordervariable. Hmm, we get an even more pronounced separation: 20 25 30.

Here we have a sudden idea: We may need to run incoming pronouns through the AudBuffer and the OutBuffer in order unequivocally to assign "dba" tags to them. When we were using separate "audpsi" concept-numbers to recognize different forms of the same pronoun, the software could pinpoint the case of a form. We no longer want different concept-numbers for the same pronoun, because we want parameters like "dba" and "snu" to be able to retrieve correct forms as needed. Using the OutBuffer might give us back the unmistakeable recognition of pronoun forms, but it might also slow down the AI program.

Before we got the idea about using OutBuffer for incoming pronouns, in the OldConcept module we were having some success in testing for "seqneed" and "pos" to set the "dba" at "4=acc" for incoming direct objects. Then we rather riskily tried setting a default "dba" of one for "1=nom" in the same place, so that other tests could change the "dba" as needed. However, we may obtain greater accuracy if we use the OutBuffer.

3. Mon.30.JAN.2012 -- Removing Engram-Gaps From Verbs

Yesterday in the Russian AI we experimented rather drastically with using the "ordo" counter to cause words of input to receive levels of activation on a descending slope, so that the AI would be inclined to generate a sentence of response starting with the same subject as the input. We discovered that the original JavaScript AI in English was not properly keeping track of the "ordo" values, so we made the simple but drastic change of incrementing "ordo" only within OldConcept and NewConcept, both of which are modules where an incoming word must go through the one or the other.

Today we have sidetracked into correcting a problem in the VerbGen module. After input with a fictitious verb, VerbGen was generating a different form of the made-up verb in response, but calls to ReEntry were inserting blank aud-engrams between the verb-stem and the new inflection in the auditory channel. By using if (pho != "") ReEntry() to conditionalize the call to ReEntry for OutBuffer positions b14, b15 and b16, we made VerbGen stop inserting blank auditory engrams. However, there was still a problem, because the AI was making up a new form of the fictitious verb but not recognizing it or assigning a concept-number to it as part of the ReEntry process.

jan26ruai

Artificial Intelligence in Russian

Thurs.26.JAN.2012 -- Insufficient Activation of Subjects

The most glaring problem in the Dushka Russian AI right now is that the AI does not fully activate the subject-pronoun when we type in a short sentence of subject, verb and object. Without a proper subject to provide parameters, the AI fails to select or generate a proper Russian verb-form.

When we type in "люди знают нас" ("People know us"), as an answer we get "ВАМ ЗНАЮТ ТЕБЯ" -- a mishmash of "to you" "they know" "you". In general, the AI seems to be taking the final object entered as input and trying to convert it into the subject for a response.

Thurs.26.JAN.2012 -- Using the "seqneed" Variable

The Russian AI is not setting a Psi "seq" flag when we enter a Russian word as the subject of a following verb. When we inspect the recent 10nov11A.F MindForth code for clues, we discover that in October of 2011 we made major improvements to the method of assigning "seq" tags. We began using the "seqneed" variable as a way of holding off on assigning a "seq" until either the desired verb or the desired noun/pronoun made itself available. However, apparently in the English JavaScript AI we wrote the "seqneed" code only for needing nouns and not yet for needing a verb. No, we did write the code, but it involved avoiding the English auxiliary verb "do", so we accidentally removed the verb-seqneed code from the RuAi. Let us put most of the code back in, and see what happens. Upshot: Once we put the code back into InStantiate, subjects of verbs once again began having a "seq" reference to the verb. The AI even skipped an adverb that we then inserted as a test.

jan13ruai

These notes record the coding of the Russian AI Mind Dushka in JavaScript for Microsoft Internet Explorer (MSIE).

1. Fri.13.JAN.2012 -- Re-thinking Word Recognition

For artificial intelligence in Russian we need to re-think the whole idea of word-recognition as previously implemented in our English AI Minds. In English we did not worry much about word-endings, but in Russian (or German) we need to recognize a verb-form regardless of the number and person in which it is encountered. Since we are using the OutBuffer mechanism to detect and recognize verb-endings, we would like to use the same mechanism to retroactively insert a provisional audpsi identifier on not just the final phoneme of an auditory word-engram but also on the final stem-phoneme and perhaps on each phoneme of the inflected verb-ending. Then we would like to modify the AudRecog module so that it holds onto the provisional audpsi and declares the recognition of a verb in whatever present-tense form it is encountered.

Now we have run the current AI with an Alert box to tell us what is the value of "audpsi" when a second-person singular verb-ending is detected. With the input of "ЗНАЕШЬ" there was no value given for "audpsi", but for "ДЕЛАЕШЬ" a value of "821" was indicated, because the verb-form in its various permutations is provided in the RuBoot sequence.

2. Sat.14.JAN.2012 -- Enhancing Auditory Input

Yesterday in the AudMem module we had difficulty in waiting for the deposition of an audpsi ultimate-tag and in trying retroactively to insert the tag on the penultimate phonemes of the Russian word being recognized. We were obtaining values for audpsi at times when we expected there to not yet be an audpsi.

Although we try zeroing out audpsi at the end of AudMem, it looks as though further use of "audpsi" is required in the AudListen module and in the AudInput module, where finally audpsi is converted to
oldpsi for use in the OldConcept module.

It turns out that AudListen calls AudInput when a space-bar is reached during keyboard entry of a word. The AudInput module, without using AudMem, directly stores an audpsi ultimate-tag retroactively by using the "tult" value. Therefore we should be trying to insert additional "audpsi" tags in AudInput and not in AudMem.

3. Sun.15.JAN.2012 -- Auditory Stem-Tagging

We have gradually learned that the AudInput module will not let us readjust values of audpsi on a word from within an if-clause testing for a value of zero on the "aud4" or ctu continuation-flag. Therefore we may need to introduce a secondary if-clause in order to make each phoneme of the word carry the audpsi tag.

We developed a suspicion that something was not letting a positive audpsi be inserted after any phoneme with an "aud4" continuation-flag "ctu" of one. We searched for "aud4 ==" and in audDamp we found the conditional "if (aud4 == 1) aud5 = 0". This obscure line of code made us spend one or two days of work in trying to comprehend why we could not "backfill" the audpsi value onto phonemes prior to the final phoneme of a word.

When we commented out the offending line in audDamp, we began to notice unwarranted carry-overs of an old audpsi onto the first phoneme of the subsequent word. To correct that problem, audpsi will need to be reset to zero in at least one additional location. Actually, we had to reset "morphpsi" to zero at the end of AudRecog to solve the problem.

4. Sun.15.JAN.2012 -- Russian Verb Stem Recognition

Now in AudRecog we need to set up provisional recognition of Russian verb-stems. We create a "provrec" variable for "provisional recognition" and we use it to detect the early presence of "audpsi" tags before the end of a word is reached. Dushka begins to recognize incoming Russian verbs and to generate incorrect but on-target sentences using the recognized verb in the infinitive form. It remains to use the AudBuffer mechanism and the parameters of person and number to generate the output of a Russian verb in the proper gramatical form.

Table of Contents (TOC)

• Fri.13.JAN.2012 -- Re-thinking Word Recognition

• Sat.14.JAN.2012 -- Enhancing Auditory Input

• Sun.15.JAN.2012 -- Auditory Stem-Tagging

• Sun.15.JAN.2012 -- Russian Verb Stem Recognition

jan12ruai

These notes record the coding of the Russian AI Mind Dushka in JavaScript for Microsoft Internet Explorer (MSIE).

Thurs.12.JAN.2012 -- Parsing Russian Verb-Endings

In our Russian JavaScript AI code heretofore we have merged the English and Russian AI Minds and we have eliminated or deactivated all the code for thinking in English. For the Dushka AI to think properly in Russian, we need to implement the OutBuffer mechanism for dealing with the inflectional endings of Russian verbs and nouns. Since we are not sure where to begin, we will present ourselves with the problem of dealing with the input of a previously unknown Russian verb.

Pressing Alt-Shift to toggle into Russian input, we ran the AI and we typed in the word "ЗНАТЬ", which the AI properly recognized as bootstrap concept #840. The AI responded with an ungrammatical sentence of "Я ЗНАТЬ МЕНЯ".

Then we typed in the word "ЗНАЮ", which the AI failed to recognize as a form of ЗНАТЬ, assigning instead a concept number of 882, as if the item was a brand new word being learned by the AI. We will try setting the value of "nru" to 900 at the end of RuBoot, so that new concepts will be learned with concept numbers starting at #901. Now we typed in "ЗНАЮ" and it was assigned #901 as a concept number. Next we typed in "ЗНАЕШЬ", and it, too, was assigned #901 as a new concept.

If we want the OutBuffer mechanism to recognize a personal verb form as such, we will need to go back to a version of the Russian AI which was sending input into the buffers. On the Packard-Bell desktop computer in the 25dec11A.F MindForth, we used the "abc" transfer-variable in the AudListen module to capture input keystrokes and move the characters into
a buffer. In the JavaScript AI, we will need to use the area of AudListen() where "pho = pho.toUpperCase()" turns each keystroke into an uppercase Cyrillic letter. From there we also call AudBuffer so that the "abc" values are transferred into the buffer.

We should probably not call OutBuffer from the "CR()" carriage-return module, which deals with the moment after an incoming word has gone into the AudMem() module. Instead we should probably deal in AudListen() directly with the input of a space-bar or a carriage-return.

We need a suitable location to reset the "phodex" counter back to zero after the end of a word of input. The "CR()" carriage-return module does not seem to effect the hange promptly enough. Let us try resetting "phodex" in the AudListen module when a carriage-return or a space-bar is entered. That method seems to work well, and somehow the AudBuffer and the OutBuffer apparently get cleared out.

Now we need to choose where the testing for any particular verb-ending in the OutBuffer will take place. It could maybe take place in the AudMem module. No, it turns out that it is somehow too late to test for a "b16" ending in AudMem. It works better if we test for "b16" in AudListen, before the character even goes into AudMem. It also turns out that we can use "if (b16==String.fromCharCode(1070))" as a way to test for an actual Russian character.

In AudListen we have now managed to build up code that tests the final three right-justified spaces in the OutBuffer and recognizes a second-person singular Russian verb-ending during keyboard input. Within the same test-code we have set the "dba" as "2" for second person and the part-of-speech "bias" and "pos" at "8" for a verb. The set values carried over into the memory arrays. Thus we expanded and improved the RuParser function. The same mechanism that recognizes a verb-ending, also parses the word as a verb.

dec30ruai

Russian AI Mind Programming Journal

These notes record the coding of the Russian AI Mind Dushka in JavaScript for Microsoft Internet Explorer (MSIE). The free, open-source Russian AI will grow large enough to demonstrate a proof-of-concept in artificial intelligence, until the intensive computation of thinking and reasoning threatens to slow the MSIE Web browser down to a crawl. To evolve further, the Russian AI Mind must escape to more powerful programming languages on robots or supercomputers.

1 Fri.30.DEC.2011 -- Russian AI Bootstrap Words

In the ru111229.html version of the Dushka Russian AI we coded the AudBuffer to load Russian characters during SpeechAct and the OutBuffer to move each Russian word into a right-justified position subject to the changing of inflectional endings based on grammatical number and case for nouns, and number and person for verbs. Next we need to determine which forms of a Russian word are ideal for storage in the RuBoot bootstrap sequence.

It seems clear that for feminine nouns like "ruka" for "hand", storage in the singular nominative should suffice, because other forms may be derived by using the OutBuffer to remove the nominative ending "-a" and to substitute oblique endings of any required length.

For regular Russian verbs in the group containing "dumat'" for "think" and "dyelat'" for "do", it should be enough to store the infinitive form in the RuBoot module, because the OutBuffer can be used to create the various forms of the present tense. If a human user inputs such a verb in a non-infinitive form, such as in "ty cheetayesh" for "you read", the OutBuffer can still manipulate the forms without reference to an infinitive. This new ability is important for the learning of new verbs. Since there is no predicting in which form a user will input a new Russian verb, the OutBuffer technique must serve the purpose of creating the verb-forms and of tagging their engrams with the proper parameters of person and number.

Since JavaScript is not a main language for artificial intelligence in robots, our Dushka Russian AI serves only as a proof-of-concept for how to construct a robot AI Mind in a more suitable language. We use JavaScript now because it can display the Russian and because a Netizen can call the AI into being simply by using Internet Explorer to click on the link of the Душка AI Mind.