pmpj1021.html

First working AI uses OutBuffer to inflect English verbs.

We have been cycling through the coding of the AI Mind in Perl, in JavaScript and in Forth. Now we are back in Perl again, and we need to implement some improvements to the EnVerbGen() module that we made in the other AI programming languages.

First of all, since the English verb generation module EnVerbGen() is mainly for adding an "S" or an "ES" to a third person singular English verb like "read" or "teach", we should start using $prsn instead of $dba in the EnVerbGen() source code. Our temporary diagnostic code shows that both variables show the same value, so we may easily swap one for the other. We make the swap, and the first working artificial intelligence still functions properly.

Now it is time to insert some extra code for verbs like "teach" or "wash", which require adding an "-ES" in the third person singular. Since we wrote the code during our cycle through JavaScript, we need only to port the same code into Perl. EnVerbGen() now uses the last few positions in the OutBuffer() module to detect English verbs like "pass" or "tax" or "fizz" or "putz" that require "-ES" as an ending.

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.

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.

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.