Computation with knowledge

 

'Brainhat' is a project to model knowledge, inference, reason and intuition, and to scale it. The objective of programming with knowledge is to build a system that "knows" things, can evaluate new information and react.

Is this AI?

The term Artificial Intelligence has come to mean high order curve-fitting with predictive capabilities. AIs are trained. The product is matrices whose contents represent the data they were trained by, structured for the program that trained them. In that sense, they're somewhat intransigent. To update an AI one has to retrain it. Accordingly, in use, they're read-only intelligence. Any sequential or stateful behavior has to be simulated by adding code around an AI.
 

Brainhat could be that kind of code; Brainhat + AI would be a powerful combination. But, Brainhat is not AI in the current conventional sense of the term.

Is this Natural Language Processing?

Maybe! But, no.... In the 1970s, Brainhat would have been called Natural Language Processing (NLP). That term has been subsequently repurposed to represent language translation, text summarization and bots. Brainhat, by contrast, processes with deep knowledge representations. Human language is valuable for conveying knowledge. But once Brainhat compiles it into a knowledge representation, the language is thrown away. So, in that sense, there's no natural language processing at all—except when communicating with you; Brainhat processes knowledge.

The Short Tour

Natural language makes knowledge portable. Here are some examples of knowledge conveyed by simple English:

 
"You want a hamburger." (proposition)

 
"The dog is hungry." (attribute assignment)

 
"I was the king." (equivalence)

Brainhat "programs" are collections of statements like these, compiled into binary form. Each statement conveys a simple idea. Taken together, they form a context--the collection of things known organized by the order in which they happened, and indexed for retrieval.
 

Brainhat processing is dataflow-driven. That means that when something becomes relevant, processing occurs automatically. This includes execution of inferences, memory loads and stores and data-sharing across the network. This is an example of an explicit inference.

"if the dog is hungry then the dog wants to eat."

When knowledge that the "the dog is hungry" meets this inference template, new knowledge will be created:

"the dog wants the dog to eat."

You probably have experience with procedural and imperative programming languages such as C, Perl or Python. These kinds of languages feature data types. The number 3, TRUE, and "11 Oak Street" are examples of data of type integer, boolean and string. For a datum to have meaning it has to be associated with a variable or table entry, as in k=3, complete=TRUE or address="11 Oak Street." Data do not live unbound; they don't float around on their own. integer i,j,k string c j = 2 k = 1 i = j + k string = "here is a non-zero answer:" if (i != 0) print string, " ", i end Procedural languages also have flow control statements, such as 'if' or 'while,' that implement branching. There may be subroutines or an event loop. The program jumps from place to place, but execution will always be somewhere within the code--somewhere that you can point to. By contrast, there are no flow control statements within Brainhat. There's no program in a regular sense. There's no distinction between data and code. Rather, the data themselves direct execution and are perhaps even the result of execution. Data do float around, unattached.

Here is a larger example. This "program" is made up of three explicit inferences, a proposition and an attribute assignment:

	      if dog has toy and dog is in room then toy is in room.
	      if a dog sees a cat then dog drops toy and dog chases cat.
	      if dog drops toy then dog does not have toy.
	      dog has a ball.
	      dog is in kitchen.
	    

The lines making up the program can be entered interactively or pre-compiled and invoked. Below, we continue interactively. The statements preceded by '>>' are entered by the user:

	      >> dog is in bathroom.
                 dog is in bathroom.
                 a ball is in bathroom.
	      >> where is ball?
                 a ball is in bathroom.
	      >> dog sees cat.
                 dog in bathroom sees cat.
                 dog in bathroom drops toy.
                 dog in bathroom does not have toy.
                 dog in bathroom chases cat
	      >> dog is in bedroom.
                 dog is in bedroom.
	      >> where is ball?
                 a ball is in bathroom.
	    

The dog had the ball at the outset, but now the dog is in the bedroom and yet the ball remains in the bathroom. Why is that? The bathroom is where the dog dropped the ball when the dog chased after the cat.
 

To read human language, Brainhat has to recognize the tokens (words) and it has to make sense of their order. Tokens and order are prescribed by a vocabulary and a grammar. The vocabulary says what the tokens can be. The grammar says how they can be combined. The vocabulary and grammar create the possibilities for, and define the limits of, what Brainhat can understand.
 
Input: "I hear the mailman." The program tags parts of speech--the subject, verb and object--and places them into a data structure that represents the statement. The knowledge is captured and the original English is discarded.

                o Root
               /|\
     SUBJECT  / | \ OBJECT
             o  |  \
            I   |   o mailman
                |
           VERB |   
                o   
              to hear 
	    

In Brainhat terminology, each of the vocabulary elements--"I", "to hear" and "mailman" is called a concept. Any combination of concepts is called a complex concept or CC. Compiling the statement "I hear the mailman" produces a CC data structure that resembles the diagram, above. As a data structure, a CC can be indexed, stored, recalled, compared and transformed:

	      >> i hear the mailman
                 You do hear the mailman.
	      >> what do i hear?
                 You do hear the mailman.
	      >> do i hear the mailman?
                 yes. You do hear the mailman.
	      >> what do i do?
                 You do ask do You hear the mailman.
	    

Language makes knowledge portable, but it can be imprecise. When I say "the dog has a ball," chances are that your mental image differs from mine--a different kind of dog, ball and location. The differences are a consequence of the knowledge we have acquired previously. Because of the ambiguity of language, contexts containing the same knowledge will often disagree on the details.

Brainhat can talk to non-Brainhat devices through a very simple API. Devices that Brainhat talks to might include robots or classifiers that we often call AIs. We can leverage the observations of AIs as knowledge. API interfaces are asynchronous. Anytime a remote device or process wishes to speak, Brainhat will accept the input and update the context. Brainhat may also request information from a remote device or instruct it to do something. The confirmation or answer can be a simple "no" or "yes" or "blue" or "in the water", and so on, or it can be complete simple English statement. Brainhat provides a response ID so that a response received can be correlated with a specific request.
 

Human language captures knowledge and makes knowledge portable. Programming Brainhat with simple English, then, is tantamount to programming with simple knowledge. This is very different than computing with data, which is what most of us are familiar with. To highlight the difference, ask yourself how would you program this in C, Perl or JavaScript?: "I love you." In Brainhat programming, this can be just the right thing to say. As a minimum, the new knowledge will be stored and indexed. It may also cause changes in preexisting stored knowledge, the exercise of implicit and explicit inferences, the recall of memories and some interprocess communication.

For some objectives, orchestrating knowledge and the execution of inferences can be complicated. If you wanted to teach Brainhat how to play a poker or make an ice cream cone, for instance, it may be quicker to write a script. For this, Brainhat has a scripting framework, that allows the user to craft an agenda to pursue specific goals. Scripts can follow many paths at once, gather information from many sources and advance many agendas. Multiple scripts can be active at one time; you may make ice cream cones at the same time as you play poker. Scripts can be traded across the network too, so it is possible to have other copies of Brainhat do your work or benefit from your scripting, and vice versa. So, if your copy of Brainhat doesn't know how to make a cone, perhaps someone else's can lend you a script.
 

Brainhat, the program

Brainhat is both a program and a programming environment. It executes as a stand-alone application or as a network daemon. As a program, Brainhat is a development platform for knowledge and knowledge-based events. As a daemon, Brainhat is a clearinghouse for knowledge events traded through an arbitrary number of connections. The Brainhat daemon may host external links to robots or sensors and user interfaces, such as speech engines.

 ---------------------------------------------------------------------
 |                                                                   |
 |                               ---- vocabulary                     |
 |                              |                                    |
 |                              |     ---- grammars                  |
 |                              |    |                               |
 |                              v    v                               |
 |                          -----------                              |
 |      command line --->  |           | <--- ext updates (text)     |
 |                         |           |                             |
 |  MS SAPI4 speech        |  Brainhat |         other               |
 |  text interface    -->  |  program  | < == >  copies              |
 |  HTML5/ws [via shim]    |           |         Brainhat            |
 |                         |           |                             |
 |            robots <-->  |           | ---> ext updates (text)     |
 |         & sensors        -----------                              |
 |                           ^   ^   ^                               |
 |                           |   |   |                               |
 |                           |   |    ----> Brainhat save sets       |
 |                           |   |                                   |
 |                           |    --- Motives                        |
 |   Brainhat                |                                       |
 |   Environment              ---> Memories                          |
 |                                                                   |
 ---------------------------------------------------------------------
			   

The Brainhat environment can include multiple copies of Brainhat sharing interprocess communication. Within a Brainhat environment, knowledge can be requested and forwarded; what one copy of Brainhat knows, the other can know, too.
 
Brainhat can't see or hear, and it only knows what it learns from interaction, through reading simple English or from other Brainhats. But, combined with AI from cameras, haptics, hearing and other senses, it can be the mind of a system that draws conclusions, reacts, communicates and changes the extant world. And, it can explain its decisions. Or, if you need something less, Brainhat can be a language parser, a human language interface, command and control for processes or a model for thought and cognition, and more. I make the piano; you play it as you like.
 

But... when?

There's documentation, two GUIs, lots of QA. It's taking years to get a release out. But, I plan for something in 2023.
 

The name Brainhat came from a spaghetti collandar with a chin strap. In the 90s, I heard a story on the radio about how students at the University of Auckland were using brainwaves to control light bulbs. That sounded interesting. I wired a pair of differential amplifiers and mounted them on top of the collandar. It worked! But I lost interest. The collandar ended up in a friend's garage.

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