PMM

PMM Artificial Intellect Systems

Our technical base is universal Proto-Mind Machine (PMM) - a general human intellect model. Proto-Mind Machine accepts mixed Text-Video-Audio stream as input and generates a corresponding Text-Audio stream as an output. Rules for output generation are being created automatically when PMM processes its input stream. The main PMM algorithm for information processing is of a neural network type that makes PMM reactions looking like human ones - through the resemblance of current and all known contexts. PMM works in two modes: Listening when it accumulates knowledge and Answering when it generates the output using its acquired knowledge.

We design artificial intelligence systems for various domains through setting up the relevant manuals in combination with learning-with-teacher method. After learning is done PMM has all necessary knowledge and begins to apply it to advice users without external help. PMM applies its knowledge, searches for data, asks questions and even makes logical inferences if it was taught to do that.

PMM can be used in any system to eliminate extra personnel that conducts relatively simple intellectual tasks. Various training systems are good examples of such applications. In general, any autonomic robotic systems which require the elements of artificial general intellect are in PMM application domain like PMM Assistant and our new PMM Thinking Engine which is a next generation of PMM Assistant (more) .

Proto-Mind Model Basis

Our method:	Creating the context connections
PMM virtualy connects any input natural language message with its messages-predecessors. These previous messages are also used as the templates of some kind for the future dialogues. PMM accepts all input messages and resolves the possible contradictions in a human-like manner. After gaining a necessary experience, PMM's answers become stable and ready for practical use.

Background:	Sophisticated model of brain network
Mathematical basis of PMM is a result of scientific research in the field of neural network models. PMM works in the same way as human brain does. The main research results are as following:
- The internal human language is universal and independent of any particular external form it can accept. It is some seed or proto-language. All natural languages spoken by humans are its external manifestations. So are English, Greek, French, etc. The formal language of modern mathematics is among them as well.
- Neural networks of human brain are the origin of proto-language, and their properties predetermine the generic proto-language structure.

Artificial Intellect:	Making an Intellectual Model of a Person
PMM is a sophisticated tool that helps to create a personal assistant for a permanent intelligence support. In order to create PMM assistant we consider a human as an abstract intellect and apply PMM to create a copy of it.

EXAMPLES
Following examples contain a set of PMM responses to the questions and corresponding situation descriptions (contexts) which are relevant to some fiction combat environment. The manual for all examples was entered in PMM once prior the tests. Find nine test cases bellow. The general structure of the examples includes: • part 1: the original situation descriptions which comprise the expected PMM reaction marked with exclamation sign, • part 2: the description of situation and a question to PMM, • part 3: a real response of PMM. Red color highlights the phrases and/or their parts which were intentionally omitted in parts 1 and 2. Blue and green colors show the difference between original situation description in the part 1 and the corresponding description in the part 2. Brown indicates a changed sentence order. Square brackets enclose the internal PMM responses (PMM internal speech). The test synopsis: 1. Tests A and B have the same question asked in the different contexts. 2. Test C shows the simplest case of logical inference made by PMM through using its knowledge. 3. Tests D, E and F demonstrate a variety of context recognition like missing/alternated words, omitted or inserted phrases, modified questions, etc. 4. Test G shows the PMM behaviour in the case it doesn't have enough knowledge to response properly. 5. Finaly the test H demonstrates the usage of the context for some kind of template creation.
• Test-A: the question "what's going on?" (the first context) Original text: halfway down the road the lead vehicles came to a sudden halt Campbell couldn't see what the problem was through the Camel's starred windshield Marchie? Tam? what's going on? bots see a checkpoint ! came Marchand's voice from the back seat looks like the local constabulary ! Provided context: down the road the vehicles came to a jerky halt Campbell couldn't see clearly what the reason was through the Camel's windshield what's going on? PMM answer: bots see a checkpoint [ came Marchand's voice from the back seat ] looks like the local constabulary

• Test-B: the same question "what's going on?" (the second context) Original text: Daz swore under his breath and at length just then however the gun fire that had become a monotonous din, trailed off in echoes simultaneously Marchand spoke in Campbell's ear hear me? Marchie, we hear you what's going on? we've rerouted into infrared through the swarmbot net, Sarge ! the EM bands are all full of noise I think the insurgents are bombing the crap out of the city's communications grid ! but our equipment is shielded great I meant what's going on here, right now? why's the firing stopped? oh! the strikebots put down the enemy bots, Sarge Provided context: Daz swore under his breath however the gunfire had become a monotonous din followed by echoes simultaneously Marchand muttered something in Campbell's headset what's going on? PMM answer: we've rerouted into infrared through the swarmbot net, Sarge [ the EM bands are all full of noise ] I think the insurgents are bombing the crap out of the city's communications grid but our equipment is shielded

• Test-C: A kind of logic inference Original text: five meters ahead of the Camel, some children were laughing as they chased two palm sized helicopter drones the drones were part of the Camel's standard kit it would have been odd to see a Camel that didn't have one or two of them circling around it these ones bounced up into the air , out of the children's reach that's momentarily putting them too far from the ground to sniff out any nearby explosive devices the drones are to be back and followed Camel ! Provided context: children were laughing as they chased tiny helicopter drones these ones bounced in the air far from the children's reach PMM answer: [ that's momentarily putting them too far from the ground to sniff out any nearby explosive devices ] the drones are to be back and followed Camel

• Test-D: General Questions Original text: it was offset from the road by wire fencing and a row of grotesquely rusted oil tanks a good sixty feet of empty lot spread out around the plant itself, which was two stories high and largely windowless they pulled in behind a screen of sun withered trees and everybody began piling out of the vehicles Campbell activated the strikebots an action that was relayed automatically to the operations situation display ! they rose up in whirs of dust Provided context: sixty feet of empty lot surrounded the plant they pulled in behind a screen of sun withered trees and everybody began getting out of the vehicles Why Campbell activated the strike bots in this situation? PMM answer: an action that was relayed automatically to the operations situation display

• Test-E: General Questions Original text: the swarmbots had rolled and scuttled their way into positions where they could deploy their Doppler radar and terahertz sensors she registered a number of heartbeats inside the generator building, only a half dozen or so in the main plant beyond the plant personnel must be hidden somewhere deeper inside, out of reach of her sensing gear ! It was a maze of metal in there, after all, so the radar didn't work at all well Provided context: the swarmbots had rolled and scuttled their way into positions where they could use their Doppler radar and terahertz sensors Desai recorded a number of heartbeats inside the generator building only six or seven in the main plant beyond where the plant personnel is hiding? PMM answer: the plant personnel must be hidden somewhere deeper inside, out of reach of her sensing gear [ It was a maze of metal in there, after all, so the radar didn't work at all well ]

• Test-F: General Questions Original text: Campbell looked more closely the two trucks were cabless behind them was a herd of squat multi wheeled platforms piled with crates and equipment these low slung cabless vehicles were aptly named scarabs each one was surmounted by a strike bot in sentry mode Ops, where are our reinforcements? reinforcements should be arriving now ! said PMM in his ear only the bots and automated vehicles got through ! Provided context: the two trucks were cabless Campbell looked more closely behind them was a set of multi wheeled platforms piled with cargo each scarabs was surmounted by a strike bot Ops, where are those damn reinforcements? PMM answer: reinforcements should be arriving now !

• Test-G: Unknown Question Provided context: Where could Kabadi see the circling UAVs? PMM answer: {No answer} Loading a new text: Mastan Nouria and Idris Kabadi had found their progress blocked by one of their own flash mobs Kabadi stepped out onto the running board of the truck to see if he could locate a path through below him Nouria was chewing a nail nervously Kabadi could see a flock of UAVs circling the white tower they had so recently left ! the British had snuck around their roadblocks and were at the compound now the streets were clear between them and the place where Kabadi and Nouria found themselves Provided context: Where could Kabadi see the circling UAVs? PMM answer: Kabadi could see a flock of UAVs circling the white tower they had so recently left

• Test-H: Parameter substitution Original text: as with all the buildings and streets in the industrial sector the water treatment plant was dotted with smart dust sensors they were supposed to be able to tell if anything bigger than a dog wandered into the area right at the moment they were obstinately reporting all clear even as insurgents with guns ran right over them it's some kind of _worm tailored to sensor nets it impedes the system functions properly the problem is that smart dust components use a worm like method tailored to sensor nets ! Provided context: as with all the buildings in the industrial sector the water treatment plant was dotted with smart dust sensors they were supposed to tell if anything bigger than a cat wandered into the area right at the moment they were reporting all clear We have some kind of virus tailored to sensor nets PMM answer: [ it impedes the system functions properly ] the problem is that smart dust components use a virus like method tailored to sensor nets !

About Us

Alexander Borzenko

Architect/Developer

1985	Got a Master degree in Applied Mathematics and Computer Science
1985 - 1989	Worked on various neural network models for visual image recognition
1987	Defended Ph.D. thesis related to mathematical model of reliability of missile control systems
1987 - 1991	Worked on mathematical neural network model of symbolic data processing
1989 - 1995	Worked on visual image recognition and processing system for space satellite
1990	Defended a post-doc thesis on symbolic data processing through neural networks
1991	Got Ph.D. in Mathematic Modeling
1995 - 1998	Worked on variety of visual recognition systems like face and signature recognition
1995 - 2000	Worked on computer methods for post-doc result implementation
2000 - 2005	Work on practical implementation of the symbolic data processing methods
1998 - 2008	Worked on general theory of symbolic data processing in human brain

Andrew Borzenko

Software Developer

2004 - 2009	Undergraduate student at the University of Toronto
2009	B.Sc. in Computer Science, University of Toronto

PMM and Artificial General Intellect

PMM creates a naturally formed logic system with only substitution rule (modus ponendo ponens).

Generally any sequence of phrases can be stored in PMM language database. This feature serves as a means for the inference machine setup. Being properly set, PMM works like some kind of artificial general intellect.

For example, PMM can process a common syllogism.

Major Premise:	All humans are mortal	PMM’s context	(1)
Minor Premise:	Socrates is a human	PMM’s key phrase	(2)
Conclusion:	Socrates is mortal	PMM’s inference	(3)

This syllogism PMM’s interpretation really works for anybody’s name (in italic) mentioned in the phrase of type (2) within the context (1) . Broadly speaking, PMM considers every text fragments like (1-3) as some “syllogism” allowing us to vary the syllogism structure relatively its formal definition. For instance, we can say “Socrates belongs to the human race” instead of (2) without causing a failure. On the contrary, the phrase “Socrates is not a human” won’t produce the conclusion (3) because of automatically detected "not" semantics.

The syllogism (1-3) is not an exception, and PMM can implement all known others. Syllogism selection by PMM depends on the context in which a key phrase has been entered. No random syllogism selection normally happens. But if it does happen the PMM reaction should be corrected (preferably at the learning stage).

Relevant Publications

[1]	A. Borzenko, “A Neural Mechanism for Human Language Processing”. Neurocomputing, special issue "Artificial brain", 2010, doi:10.1016/j.neucom.2009.11.051.
[2]	A. Borzenko, “Neuron Mechanism of Human Languages”. In: IEEE Proceedings of International Joint Conference on Neural Networks (IJCNN), Atlanta, 2009, ISBN: 978-1-4244-3548-7. (reference)
[3]	A. Borzenko, “Language Processing in Brain Networks”. In: Proceedings of the First AGI Conference, IOS Press, Volume 171 Frontiers in Artificial Intelligence and Applications, Edited by: P. Wang, B. Goertzel and S. Franklin, February 2008, 520 pp., ISBN: 978-1-58603-833-5. (reference)
[4]	A. Borzenko, “Formal Neuron System for the Natural Language Analysis”. In: IEEE Neural Networks Proceedings, 1998, p.2561-2564.
[5]	A. Borzenko, “Associative Recognition of Signals by a Network of Formal Neurons”. In: Automatics and Mechanics, No. 1, 1985, Moscow, Science, p. 95-100.