Supergoals

Anti-goals

I cannot find it now on your site, but, it seems your system has or will have the opposites to goals (was it goals with negative desirability?)

Answer:In general, same supergoal works in both negative and positive directions.
Super goal can give both positive and negative reward to the same concept.
For example, supergoal "Want more money" could give negative reward to "Buy Google stock" concept, responsible for investment money into Google stock, because it caused money spending. One year later same "Want more money" supergoal may give positive reward to the same "Buy Google stock" concept, because this investment made the system richer.

Supergoal: "can act" or "state only"?

Supergoals can act. Supergoal actions are about modification of softcoded goals.
Usually Supergoal has state. Typically supergoal state keeps information about supergoal satisfaction level is at this moment. Supergoal may be stateless too.

Glue for the system

it seems to me, that you use cause-effect relations as a glue to put concepts together, so they form a connected knowledge; is it the only glue your system has?

Yes, correct: cause-effect relations are the only glue to put concepts together.
I decided to have one type of glue instead of many types of glue.
It's easier to work with one type of glue.

At the same time I have something else that you may
consider a glue for the whole system:
1) Desirability attributes (softcoded goals)- keep information about system's priorities.
2) Hardcoded units - connect concepts to the real world. Super goals are the special subset of these hardcoded units.

What AI ideas has Google introduced?

Google not introduced, but practically demonstrated the following ideas:

1) Words are the smallest units of intelligent information. Word alone has meaning. Letter alone - doesn't. Google searches for words as a whole. Not for letters of substrings.

2) Phrases are important units of information too. Google underlines importance of phrases by supporting search in quotes, like "test phrase".

3) Natural language (plain text) is the best way to share knowledge between intelligent systems (people and computers).

4) Programming languages that are the best for mainstream programming - the same languages are the best for intelligent system development. LISP, Prolog, and other artificial programming languages are less efficient in intelligence development than mainstream languages like C/C++/C#/VB/: (Google proved this idea by using plain C as a core language for "advanced text manipulation project".

5) Huge knowledge base does matter for intelligence. Google underlines importance of huge knowledge base.

6) Simplicity of knowledge base structure does matter. In comparison with CYC's model, Google's model is relatively simple. Obviously Google is more efficient/intelligent than dead CYC.

7) Intelligent system must collect data automatically (by itself, like in Google's crawler). Intelligent system should not expect to be manually fed by developers (like in CYC).

8) To improve information quality, intelligent system should collect information from different types of sources. Google collects web pages from web, but also it collects information from Google toolbar - about what web pages are popular among users.

9) Constant updates and forgetting keeps intelligent system sane (Google constantly crawls the Web, adds new and deletes dead web-pages from its memory).

10) Links (relations) add intelligence to a knowledge base (Search engines made the Web mode intelligent);
Good links convert knowledge base into intelligent system (Google's index with web work as a very wise adviser (read: intelligent system)).

11) Links must have weights (like in Google's Page rank). These weights must be taken into consideration in decision making.

12) Couple of talented researchers can do far more than lots of money in wrong hands. Think about "'Serge Brin & Larry Page search' vs 'Microsoft's search'".

13) Sharing ideas with public helps research project to come to production. Hiding ideas - kills the project in the cradle. Google is very open about its technology. And very successful.

14) Targeting practical results helps research project a lot. Instead of having "abstract research about search", Google targeted "advanced web-search". Criteria of success of the project were clearly defined. As a result Google project quickly hit production and generated tremendous outcome in many ways.

How does strong AI schedule super goals?

Strong AI doesn't schedule super goals directly. Instead strong AI schedules softcoded goals. To be more exact, super goals schedule softcoded goals by making them more/less desirable (see Reward distribution routine). The more desirable softcoded goal is – the higher probability is that this softcoded goal will be activated and executed.

How strong AI finds a way to satisfy super goal

The idea is simple: whatever satisfies super goal now -- most probably would satisfy the super goal in the future. In order to apply this idea, super goals must be programmed in a certain way. Every super goal itself must be able to distinguish what is good and what is bad.
Such approach makes super goal kind of "advanced sensor".
Actually not only "advanced sensor", but also "desire enforcer".

Here's the example how it works:
Super goal’s objective: to be rich.
Super goal sensor implementation: check strong AI’s bank account for amount of money on it.
Super goal enforce mechanism: mark every concept which causes increasing the bank account balance as "desirable". Mark every concept which causes decreasing the bank account balance as "not-desirable".

Note: "mark concept as desirable/undesirable" doesn't really work in "black & white" mode. Subtle super goal enforcement mechanism either increases or decreases desirability of every cause concept affecting the bank account balance.

Concept type

Your concepts have types: word, phrase, simple concept and periheral device. What is a logic behind having these types?
In fact "peripheral device" is not just one type. There could be many peripheral devices.
Peripheral device is a subset of hardcoded units
Concept can be of any hardcoded unit type.
Moreover, one hardcoded unit can be related to concepts of several types.
For example: text parser has direct relations with concept-words and concept-phrases. (Please don't confuse these "direct relations" with relations in the main memory).
Ok, now we see that strong AI has many concept types. How many? As many as AI software developer code in hardcoded units. 5-10 concept types is a good start for strong AI prototype. 100 concept types is probably good number for real life strong AI. 1000 concept types is probably too many.

So, what is a "concept type"? Concept type is just a reference from concept to hardcoded unit. Concept type is a reference from concept to real world through a hardcoded unit.

What concept types shold be added to strong AI?
If AI developer feels that concept type XYZ is useful for strong AI...
and if the AI developer can code this XYZ concept type in hardcoded unit...
and if this functionality is not implemented in other hardcoded unit yet...
and the main memory structure doesn't have to be modified to accomodate this new concept type...
then the developer may add this XYZ concept type to strong AI.

What concept types should not be added?
- I feel that such concept types as "verb" and "noun" should not be added, because there is no clear algorithm to distinguish between verbs and nouns.
- I feel that "property concept type" should not be used, because "property concept type" is already covered by "cause-effect relationships" and because implementation of property type concepts will make main memory structure more complex.

How naked is a concept?

There is a concept ID, which you use when referring to some concept. When coding, everyone will have these IDs, the question is how "naked" they are, i.e. how they are related to objective reality.

Concept alone is very naked. Concept ID is a core of a concept.
Concept is related to objective realitythrough relations to other concepts.
Some concepts related to objective reality through special devices.
Example of such device could be text parser.
Example of connection between concept and objective reality: temperature sensor connected to temperature sensor concept.

What learning algorithms does your AI system use?

Strong AI learns in two ways:
1. Experiment.
2. Knowledge download.
See also: Learning.

What do you use to represent information inside of the system?

From "information representation" point of view there are two types of information:
1) Main information - information about anything in the real world.
2) Auxiliary information - information which helps to connect main information with the real world.
Examples of auxiliary information: words, phrases, email contacts, URLs, ...

How main information is represented

Basically main information is represented in form of concepts and relations between concepts.
From developer's perspective all concepts are stored in Concept
table. All relations are stored in the Relation table.

Auxiliary information representation

In order to connect main information to the real world AI needs some additional information. Like human brain's cortex cannot read, hear, speak, or write --- the same way main memory cannot directly be connected to the real world.
So, AI needs some peripheral devices. And this devices needs to store some internal information for itself. I name all this information for peripheral devices: "auxiliary information".
Auxiliary information is stored in the tables designed by AI developer. These tables are designed on the case-by-case basis. Architecture of a peripheral module is taken into consideration.
For example, words are kept in WordDictionary table, phrases are kept in PhraseDictionary table.
As I said: auxiliary information connects main information with the real world.
Example of such connection:
Abstract concept of "animal" can relate to concepts "cat", "tiger", and "rabbit". Concept "tiger" can be stored in the word dictionary.
In addition to that Auxiliary information may or may not be duplicated as main information.
Text parser may read word "tiger", find it in the word dictionary: Then AI may meditate on the "tiger" concept and give back some thoughts to the real world.

AI tools

Internal and external tools

Internal tools

Internal tools are such tools which are integrated into AI by AI developer.
Example of human's analogue would be a hand + motion part of the brain, which human has since birthday. Another example: eyes + vision center of the brain --- this vision tool is also integrated into human's brain before the brain starts to work.

External tools

External tools are such tools which are integrated into AI by AI itself. AI learns from its own experience or from external knowledge about the tool, then practice to use the tool, and then use it.
Example of human's analogue here would be an axe. Another example could be calculator.

Indistinct boundaries between Internal and External tools

How would you classify "heart pacemaker"? Without this tool some people cannot live. Also human doesn't have to learn about use heart pacemaker. At the same time humans don't get "heart pacemaker" with their body. Is it external or internal tool for humans?

In case of AI intermingling between internal and external tools is even deeper, because AI is pretty flexible.
For example, AI can learn about advanced math tool from an article in magazine, and then integrate itself with this tool. Such integration can be very tight since computers have very extendable architecture (in comparison with humans). So, "external tool" can become "internal tool".

Internal tools

Importance of internal tools

Internal tools are very important for AI because mind cannot communicate with the world without tools. External tools are unavailable for a mind without internal tools.

Internal tools integration with AI

Internal tools are connected with the mind through a set of neurons. This set of neurons is associated with the tool. When the set is active - the tool is active. When the tool is active then set of neurons is active.
Example:
Let consider internal tool integration on example of "chat client program" (like ICQ, MSN, or Yahoo messenger).
"Chat client program" is represented in the main memory by neuron nChatClientProgram.
If AI decided to chat then AI activates nChatClientProgram neuron. That activates "chat client program" (the tool). The tool reads active memory concepts, converts them into text and sends text message over internet. After that the tool activates neuron nChatClientProgramAnswerWaitMode in the main memory.
When the tool gets response from Internet, then the tool:
- Parses incoming text and put received concepts into the short memory.
- Activates neuron nChatClientProgramAnswerReceived.
Activation of nChatClientProgramAnswerReceived causes execution of softcoded routine associated with nChatClientProgramAnswerReceived neuron.
After execution, the results are evaluated against AI's super goals. AI learns from the experience, in particular:
1. Desirability of nChatClientProgram, nChatClientProgramAnswerWaitMode, nChatClientProgramAnswerReceived, and other related neurons are evaluated (see Reward distribution routine). Successful chatting experience would increase desirability of nChatClientProgram neuron and therefore probability of "Chat client program" use in the future. Unsuccessful experience would reduce probability of such use.
2. Softcoded routines are evaluated and modified. Modified routine can be applied to process results of the next incoming message.

List of internal tools to develop for strong AI

1. Timer. It's good to have internal sense of time.
2. Google search - helps to understand new concepts.
3. Chat with operator.
4. Internet chat client.

External tools

Importance of external tools

External tools are important because:
1) There could be millions of external tools.
2) AI can use already developed humans' tools.
3) External tools can be converted into internal tools and gain all advantages of internal tools.

External tools integration with AI

External tools are connected with the mind through internal tools.
Example:
Internal tool: web browser.
External tool: stock exchange web site.
Through internal tool AI can use external tool.

Story of my interest in AI

Jiry> When did you first decide to attempt making Strong AI?
Jiry> Was there anything particular what triggered that decision?

I'd say it was ~year 2001.
It wasn't sudden decision.
I was interested in AI among many other things.
Gradually I recognized how powerful could such tool be.
Also I decided that since computers are getting more and more
powerful, AI should be implemented pretty soon.

Originally I didn't think that I should develop AI, I just thought
that I'll be among early adopters of AI, that I will just tweak it after someone
(probably Microsoft) would develop AI framework.

Gradually I understood that I have to build AI by myself, because:
1) practically all other researchers go in wrong directions.
2) I learned about approaches which should give successful results and
put approximate AI model together.

AI operator

What are the responsibilities of AI's operator?

AI developer can define some default values for parameters like:
- how quickly should AI system forget new information.
- what weight increment should be applyed to relation between two concepts which were read near each other.
- ...

AI will be able to work with these default values, but in order to achieve optimal performance, AI operator has to tweak these default values.
Operator will observe and analyze how AI performs, modify default values, and see for improvements in AI's mental abilities.

"AI's operator" is not the person who talks with AI all the time.
"AI's operator" almost doesn't talk with AI.
"AI's operator" observes how AI's mental process works. Also "AI's operator" "tunes/tweaks" AI's mind.

See also:
AI's operator

AI answering comlex questions

> Imagine that the example talks about 2 accounts, initial amount $100
> on both and several simple financial transactions between the
> accounts. I believe your AI would get confused very soon and would not
> be able to figure out the balance.

In the situation of such complexity regular human beings cannot provide adequate answer.
What do you expect from AI under development?

If we are talking about perfect AI now, then again --- AI will not read text with "one-time parsing" approach.
Instead, perfect AI will read like human: read sentence, think, make decision whether to read father, or re-read again, or skip reading at all, or use another source of information (e.g. ask questions or go to Google), or do anything else. Perfect AI would accomplish chosen action until AI would be satisfied with the results.

But let's return back to today's reality: we are talking about developing first AI prototype, so we'd better skip too complex tasks for now.

How to translate text from one language to another

Language translator prototype
0) Originally we have a sentence in a source language and we want to translate it into a destination language.
1) Take "source language" sentence.
2) Find all text concepts (words and phrases) in the source sentence.
3) All these text concepts constitute "source language text thought".
4) Search for all concepts which are related to the source language text thought.
5) As a result, we'll get set of concepts which conctiture abstract thought.
6) Now it's time to search for related text thought in destination language.
7) So, we search all concepts which simultaneously:
a) Relate to this abstract thought.
b) Relate to the concept which represents destination language.
8) At this point we have all concepts related to the original text and to the destination language. This is "destination language text thought".
9) Now we can eaily convert this "destination language text thought" into "destination language text".
Strong AI can build the final sentence (by using a word dictionary, a phrase dictionary, and text pairs dictionary).
See also:
Text synthesizer.

(Originally written: Sep 2004).

Mistakes and general intelligence

"People make stupid mistakes. A well designed AI should not."
Jiri Jelinek

Human beings make mistakes because their minds make approximate decisions.
Human beings have general intelligence because their minds able to make approximate decisions.

If you develop AI without this critical feature (approximate decision making) then such AI wouldn't have general intelligence...

Flawless AI

In order to make decisions without mistakes you need 3 things:
1) Appropriate "perfect problem solver" algorithm.
2) Full information about our word.
3) Endless computational power.

Even if #1 is theoretically possible, #2 and #3 are impossible even in theory.

Abstract concept

Abstract concept is a concept which is not directly connected to system's receptors.
Abstract concept is connected with other concepts though. Abstract concept is connected to receptors indirectly through non-abstract concepts (surface concepts).

It's not easy task to identify and create an abstract concept. You cannot just borrow it from external world as surface concepts.

What do you think: is it good idea to name such abstact concept as Deep Concept?
It may help to distinguish abstract concepts which are available in books from abstract concepts which must be created by AI itself.

Limited AI, weak AI, strong AI

Jiri,

> your AI reminds me of an old Czech fairy-tale where a dog and cat
> wanted to bake a really tasty cake ;-9, so they mixed all kinds of
> food they liked
> to eat and baked it.. Of course the result wasn't quite what they expected >;-).

That's not the case.
:-)

I know a lot of stuff and I carefully selected features for strong AI.
I rejected far more features than I included.
And I didn't it because I thought that these rejected features are useless in true AI, in spite that these rejected features are useful for weak AI.

> I think you should start to play with something a bit less challenging
> what would help you to see the problem with your AI.

Totally agree.
As I said --- I'm working on limited AI. Which is simultaneously:
1) Weak AI.
2) Few steps toward strong AI.

There are many weak AI applications. Some of weak AIs are steps toward strong AI, most of weak AIs don't contribute almost anything to strong AI.
That's why I need to choose limited AI functionality carefully.

Your suggestion below may become a good example of such limited AI. With proper system structure.

But probably I wouldn't work on it in the nearest future because it doesn't have much business sense.
======= Jiri's idea =======
How about developing a story generator. User would say something like:
I want an n-pages long story about [a topic], genre [a genre].
Then you could use google etc (to save some coding) and try to
generate a story by connecting some often connected strings.
Users could provide the first sentence or two as an initial story trigger.
I do not think you would generate a regular 5 page story when using
just your statistical approach. I think it would be pretty odd
mix of strings with pointless storyline = something far from the
quality of an average man-made story.
===========================

Lojban vs programming languages vs natural language

Ben, this idea is wrong:
-----
Lojban is far more similar to natural languages in both intent, semantics and syntax than to any of the programming languages.
-----

Actually Lojban is closer to programming languages than to natural languages.
Structure of Lojban and programming languages is predefined.
Structure of natural languages is not predefined. Structure of a natural language is defined by examples of using this natural language. This is the key difference between Lojban and Natural Language.

Since structure of natural language is not predefined, you cannot put language structure into NL parser code. Instead you need to implement system which will learn rules of natural language from massive amount of examples in this natural language.

You are trying to code natural language rules in text parser, aren’t you?
That’s why you theoretically can parse Lojban and programming languages, but you cannot properly parse any natural language even theoretically.


If you want properly parse natural language, you need predefine as little rules as possible.
I think that natural language parser has to be able to recognize words and phrases.
That's all that NL text parser has to be able to do.

All other mechanisms of natural language understanding should be implemented outside the text parser itself.
These mechanisms are:
- Word dictionary and phrase dictionary (too serve as a link between natural language (words, phrases) and internal memory (concepts).
- Relations between concepts and mechanisms which keep these relations up to date.

Lojban

Ben,

I think that it's a mistake to teach AI to any language other than
natural language.

Lojban is not a natural language for sure (because it wasn't really
tested for variety of real life communication purposes).

The reasons why strong AI has to be taught to a natural language, not to Lojban:
1) If AI understands natural language (NL) then it's a good sign that
the core AI design is correct and quite close to optimal.
If AI cannot learn NL then it's a sign that core AI design is wrong.
If AI can learn Lojban --- it proves nothing from strong AI standpoint.
There are a lot of VB, Pascal, C#, C++ compilers already. So what?

2) NL understanding has immediate practical sense.
Understanding of Jojban has no practical sense.

3) NL text base is huge.
Lojban language text base is tiny.

4) Society is "the must" component of intelligence.
Huge amount of people speaks/write/read NL.
Almost nobody speaks Lojban.

Bottom line:
If you spend time/money on design/teaching AI to understand Lojban ---
it would be just a waste of your resources. It has neither strategical nor tactical use.