VisRecog

This TikTok video thumbnail photo shows the AGI Mind coder Arthur Murray, known on the Internet as Mentifex (Latin for Mindmaker), serving in the U.S. Army as a Nuclear Weapons Electronics Specialist at the 23rd Ordnance Company of the 101st Ordnance Battallion in Heilbronn, Germany. Mentifex graduated first in his electronics class at the Redstone Arsenal in Alabama USA, because he had alfeady been studying electronics as an independent scholar in artificial intelligence. Mentifex then barely graduated at the bottom of his nuclear weapons class in the Nuclear Training Directorate at Sandia Base in Albuquerque, New Mexico, because there were too many boring details to memorize about Test and Handling gear for nuclear weapons. When Mentifex arrived for duty at the 101st Ordnance Battalion in Germany, the clerks told him that they would have to call up some soldier out in the boonies and inform him that he no longer had the highest General Technical score in the battalion. So Mentifex was intelligent enough to serve in the Army, but not especially intelligent among computer scientists working on AGI.

Computer vision systems are generally not programmed in Forth, which is an old programming language with a history of being used for robots. But Forth is a major AI language because of MindForth, which is designed to be the brain of an intelligent robot, just as ghost.pl in Perl is meant for intelligent webservers.

If a webserver maintained in Perl has a Perl-minded robot working in a control room, the Perlbot will need visually to examine and recognize and then name various objects found in its physical environment.

The ghost.pl Mind thinks in both English and Russian. When the Ghost in the Machine wants to think or talk about a seen object, the Visual Recognition Mind Module remembers the name of the object and reports it to the linguistic mind-module generating an idea or a thought about the seen object.

The Mentifex TikTok AI video #43 about the VisRecog mind-module was uploaded at
https://www.tiktok.com/@thesullenjoyshow/video/7226006786749304106
with the following script which describes the VisRecog mind-module.

Although computer vision for robots is already extremely advanced and incredibly sophisticated, it needs to be integrated with what we claim is currently a Standard Model of AGI, or Artificial General Intelligence. AGI Minds like MindForth and ghost.pl in Perl have the rudimentary stub of a Visual Recognition module, called VisRecog. The VisRecog mind-module joins together the name of a recognized object with a linguistic mind-module that is generating an idea or statement about the recognized object. These AGI Minds that will discuss a recognized object in English, or German, or Russian or Latin were created and put into the public domain by Arthur Murray, who served in the U.S. Army as a Nuclear Weapons Electronics Specialist. To win the AI arms race, integrate your computer vision system with Artificial General Intelligence.

clpm0709

Extrapolating from the First Working AGI

Artificial General Intelligence (AGI) has arrived in MindForth and its JavaScript and Perl symbionts. Each Mind is expanding slowly from its core AGI functionality. The MindBoot sequence of innate concepts and ideas can be extended by the machine learning of new words or by the inclusion of more vocabulary in the MindBoot itself.

We may extrapolate from the current MindBoot by imagining a Perlmind that knows innately the entire Oxford English Dictionary (OED) and all of WordNet and all of Wikipedia. Such an AGI could be well on its way to artificial superintelligence.

If there is no upper bound on what a First Working AGI may know innately, why not make full use of Unicode and embed innately the vocabulary of all living human languages? Then go a step further and incorporate (incerebrate?) all the extinct languages of humanity, from LinearB to ancient Egyptian to a resurrected Proto-European. Add in Esperanto and Klingon and Lojban.

jmpj0317

Restoring JavaScript variable-comments and removing obsolete variables.

Since we assume that many people have made copies of the JavaScript Artificial Intelligence (JSAI) in order to study it, today we carefully make some pressing changes and for each change we provide an explanation by way of justification. First we delete some previously commented-out code-lines which were left in the open-source AI codebase for the sake of continuity, that is, to show that the particular lines of code were on the way out. Thus we remove the commented-out variable "kbyn" from 30jun2011.

Next from the obsolete 20mar07A version of the JSAI we restore comments for some variables and we remove some obsolete variables. We add a link to Consciousness above the Control Panel.

Anyone finding a bug in the AI software may subscribe to the mail-list agi@listbox.com for Artificial General Intelligence (AGI) and report bugs to the AGI community or engage in archived AGI discussion. There is no bug-bounty, other than the glory of the deed.

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.

pmpj0408

Retroactively setting associative $seq tags for direct objects of verbs.

In the ghost194.pl AI, we have a problem where the direct-object $seq of a verb is being indeed properly assigned for human user input, but not for reentrant ideas being summoned from experiential memory. Because the $seq is not yet known when a verb comes in, the $seq value must be assigned retroactively when the direct object of the verb comes in. The situation where the process works for human input but not for a reentrant idea, suggests that the cause of the problem could simply be that the value of some pertinent variable is not being reset as needed.

This problem of the retroactive assignment of the associative $seq tag for a verb is difficult to debug. It may involve making the reentry routine equal to the human-input routine, or it may involve porting into Perl some special code from the 24jul14A.F version of MindForth. We have meanwhile been offering in the computer-science compsci subReddit a suggestion that students in need of an undergraduate research project might look into the Ghost AI software coded in Strawberry Perl 5 as an opportunity to select a mind-module to work on. We feel some urgency to debug our code and get it working as well as possible when we are inviting undergraduate students and graduate students and professors to take over and maintain their own branch of the AI Mind development. There is a steep learning curve to be surmounted before participants in such an artificial general intelligence (AGI) project may move forward in AI evolution. So now we go back to the problem of debugging the retroactive assignment of $seq subsequent-concept tags.

We search our ghost194.pl source code for "$psy[" as any instance where a $seq is being inserted either currently or retroactively into a flag-panel row of the @psy conceptual array. We discover that a $verbcon flag for seeking direct or indirect objects is governing the storage of the $seq tag in the Parser() module. Immediately we suspect that the $verbcon flag is perhaps being set during actual human user input but not during the reentry of an idea retrieved from memory. We check and we see that $verbcon is set to unity ("1") in the Parser() module when the part-of-speech $pos variable is at a value of "8" for a verb. The $pos value is set in the OldConcept() module when a known verb is recognized.

We insert a diagnostic message about the direct object in the Parser() module, and the message shows up during human user input, but not during reentry. Apparently the Parser() module is not even being called during reentry. No, it is being called, but the $verbcon flag is not being set properly during reentry. When we comment out the reset of $verbcon at the end of the AudInput() module and we move the reset to the Sensorium() module, we start seeing the assignment of direct-object $seq tags during the reentry of ideas recalled from memory. However, in a later session we must deal with the new problem that improper direct-object $seq flags are being set for personal pronouns during human user input. No, we debug the problem now, simply by resetting time-of-verb $tvb at the start of the EnThink() module, to prevent an output-sentence from adjusting associative tags for a previous sentence with a previous time-of-verb. The AI becomes able to receive "i know you" as input and then somewhat later say "YOU KNOW ME."

mfpj0827

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

Sat.27.AUG.2016 -- Creating the MindGrid trough of inhibition

In agi00031.F we are trying to figure out why we have lost the functionality of ending human input with a 13=CR and still getting a recognition of the final word of the input. We compare the current AudMem code with the agi00026.F version, and there does not seem to be any difference. Therefore the problem must probably lie in the major revisions made recently to the AudInput module.

From the diagnostic report messages that appear when we run the agi00031.F, it looks as though the 13=CR carriage return is not getting through from the AudInput module to the AudMem module. When we briefly insert a revealing diagnostic into the agi00026.F AudMem start, we see from "g AudMem: pho= 71" and "o AudMem: pho= 79" and "d AudMem: pho= 68" and "AudMem: pho= 13" that the carriage-return is indeed getting through. Therefore in AudInput we need to find a way of sending the final 13=CR into AudMem. Upshot: It turns out that in AudInput we only had to restore "pho @ 31 > pho @ 13 = OR IF \ 2016aug27: CR, SPACE or alphabetic letter" as a line of code that would let 13=CR be one of the conditions required for calling the AudMem module.

Next in the InStantiate module we need to remove a test that only lets words with a positive "rv" recall-vector get instantiated, because we must set "rv" to zero for personal pronouns being re-interpreted as "you" or "I" during communication with a human user. Apparently the Perlmind just ignores the engrams with a zero "rv" and finds the correct forms with a search based on parameters.

Now we would like to see how close we are to fulfilling all the conditions for a proper "trough" of inhibition in the AI MindGrid. When we run the ghost175.pl Perl AI and we enter "You know God," we see negative activations in thepresent-most trough of both the input and the concepts of "I HELP KIDS" as the output. In the Forth AGI, we wonder why do not see any negative activations in the present-most trough. Oh, we were not yet bothering to store the "act" activation-level in the Forth InStantiate module. We insert the missing necessary code, and we begin to see the trough of inhibition in both the recent-most input and the present-most output.