Massive words/phrases database publishes by Google

Google research publishes their massive words/phrases database:
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All Our N-gram are Belong to You
We processed 1,011,582,453,213 words of running text and are publishing the counts for all 1,146,580,664 five-word sequences that appear at least 40 times. There are 13,653,070 unique words, after discarding words that appear less than 200 times.
Watch for an announcement at the LDC, who will be distributing it soon, and then order your set of 6 DVDs.
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This team can be contacted at: ngrams@google.com

Motivational system

1) I agree that direct reward has to be in-built
(into brain / AI system).
2) I don't see why direct reward cannot be used for rewarding mental
achievements. I think that this "direct rewarding mechanism" is
preprogrammed in genes and cannot be used directly by mind.
This mechanism probably can be cheated to the certain extend by the
mind. For example mind can claim that there is mental achievement when
actually there is none.
That possibility of cheating with rewards is definitely a problem.
I think this problem is solved (in human brain) by using only small
dozes of "mental rewards".
For example, you can get small positive mental rewards by cheating your
mind to like finding solutions to "1+1=2" problem.
However, if you do it too often you'll eventually get hungry and would
get huge negative reward. This negative reward would not just stop you
doing "1+1=2" operation over and over, it would also re-setup your
judgement mechanism, so you will not consider "1+1=2" problem as an
achievement anymore.

Also, we all familiar with what "boring" is.
When you solve a problem once - it's boring to solve it again.
I guess that that is another genetically programmed mechanism with
prevents cheating with mental rewards.

3) Indirect rewarding mechanisms definitely work too, but they are not
sufficient for bootstrapping strong-AI capable system.
Consider a baby. She doesn't know why it's good to play (alone or with
others). Indirect reward from "childhood playing" will come years later
from professional success.
Baby cannot understand human language yet, so she cannot envision this
success.
AI system would face the same problem.

My conclusion: indirect reward mechanisms (as you described them) would not be
able to bootstrap strong-AI capable system.

Back to real baby: typically nobody explains to baby that it's good to play.
But somehow babies/children like to play.
My conclusion: there are direct reward mechanisms in humans even for
things which are not directly beneficial to the system (like mental
achievements, speech, physical activity).

(from AGI email list).

Richard Loosemore - Reward

Richard Loosemore (rpwl at lightlink.com):
All thinking systems do have a motivation system of some sort (what you
were talking about below as "rewards"), but people's ideas about the
design of that motivational system vary widely from the implicit and
confused to the detailed and convoluted (but not necessarily less
confused).
===

Reward

Colloquium on the Law of Transhuman Persons

Colloquium on the Law of Transhuman Persons

There are photos here how they disscussed law related to transhumans. Florida's beach pictures included :-)

How to prevent bad guys from using results of AI reserch?

David Sanders> I would like to see a section up on your site about the downsides of AIS and what preventative limits need to take place in research to ensure that AIS come out as the "good" part of humans and not the bad part. The military is already building robotic, self propelled and thinking vehicles with weapons.

Recipe for "safe from bad guys research" is the same as recipe for any
research: openness.

When ideas are available for society - many people (and later many
machines) would compete in implementation of these ideas. And society
(human society / machine society / or mixed society) - would setup
rules which would prevent major misuse of new technology.


David Sanders> How long do we really have before an AIS, demented or otherwise) decides to eliminate its maker?

Why would you care?
Some children kill their parents. Did our society collapsed because of
that?

Some AISes would be bad. Bad not just toward humans, but toward other
AISes.
But as usual --- bad guys wouldn't be a majority.

David Sanders> As countless science fiction stories have told us, even the most innocent of actions by an AIS may spell disaster,

1) These are fiction stories.
2) Some humans can cause disasters too, so what?

David Sanders> because like I said above the don't fundamentally understand us, and we don't understand them.

Why wouldn't AISes understand humans?

David Sanders> We will be two completely different species, and they might not hold the same sanctity of life most of us are born with.

Humans are not born with sanctity. Humans gain it (or not gain) while
they grow.
Same would apply to machines.

Discussion about AIS weaknesses

This discussion inspired by web-page Weaknesses of AIS.

David Sanders> AIS cannot exist (for now) without humans.

That’s not really a weakness, because time span of this weakness would be pretty short. Right now strong AI systems exist only in our dreams. :-) Within ~20 years of creating strong AI, many AIS-es would be able to survive without humans. Please, note that AIS-es would not kill humans. There would be benefits of human-AIS collaboration for all sides. This is completely different topic though. :-)

David Sanders> If they fail to understand and appreciate the human world...

If you don't understand and appreciate human world of Central Africa... would it harm you?
May be you mean "If AIS-es don't understand human world at all"? But in this case what would these AIS-es understand? And what would mean that these not-understanding systems intelligent?

David Sanders> [AIS-es] Not able to perceive like a human. They cannot hear, see, feel, taste or smell like a human.

Not true. Only first and limited versions of AIS-es wouldn’t be able to perceive like a human. Sensor devices are not too hard to implement. The major problem is implementation of Main Mind for AIS.

David Sanders> They can only feel these things like they imagine they do. Again, this makes them fundamentally incongruous with humans and I don't believe its something you can "teach around." Try to explain what "blue" is to someone who never had sight.

Have you ever seen "black hole", "conscience", or "electron"? Yet you know what they are, don't you? :-)
Blind person can understand what "blue" means: "sky is blue", "water is blue", ...

David Sanders> Until AIS have robot bodies / companions, they rely on humans for natural resources. However, once the singularity hits, that probably won't matter anymore. It is not inconceivable to think of a time in 200-500 years there are no more humans, just AIS.

Humans would probably exist long after strong AI is created. Humans just would not be the most intelligent creatures anymore :-)

David Sanders> I disagree with AIS and natural selection. I think this will happen on its own by their very nature.

AIS-es can be influenced by natural selection as much as all other living organisms. But humans had millions of years of natural selection. When would AIS-es have that much?

David Sanders> AIS will be more open about self modification as you point out. AIS will be able to make other AIS and will soon learn how to evolve themselves very quickly.

"Evolving themselves" is part of artificial selection, not natural selection.

Matt Bamberger - Matt Bamberger

Matt Bamberger - Matt Bamberger

Matt worked for Microsoft, tried to retire ... unsuccessfully, so he works again and has extensive software development experience. Matt is interested in AGI (Artificial General Intelligence) and Singularity.

An Integrated Self-Aware Cognitive Architecture

That looks like a very interesting project in a Strong AI field.
Though I (Dennis) personally disagree with couple of basic ideas here.
1) It seems that Alexei Samsonovich pays a lot of attention to self-awareness.
For me it's not clear why self-awareness is more important than awareness about surrounding world in general.
2) Another questionable thing is about AI being autonomous.
As far as I know, there is no intelligent system which is autonomous from the society. Human's baby would never become intelligent without society.
In order to make AI system intelligent, Alexei Samsonovich would have to connect the system to society somehow. For example through the Internet.

Anyway, the following looks like great AI project.
You may want to try to take part in it.

From: Alexei V Samsonovich

samsonovich

@

cox.net

Date: Tue, 18 Oct 2005 06:02:46 -0400
Subject: GRA positions available

Dear Colleague:

As a part of a research team at KIAS (GMU, Fairfax, VA), I am searching
for graduate students who are interested in working during one year,
starting immediately, on a very ambitious project supported by our
recently funded DARPA grant. The title is "An Integrated Self-Aware
Cognitive Architecture". The grant may be extended for the following
years. The objective is to create a self-aware, conscious entity in a
computer. This entity is expected to be capable of autonomous cognitive
growth, basic human-like behavior, and the key human abilities including
learning, imagery, social interactions and emotions. The agent should be
able to learn autonomously in a broad range of real-world paradigms.
During the first year, the official goal is to design the architecture,
but we are planning implementation experiments as well.

We are currently looking for several students. The available positions
must be filled as soon as possible, but no later than by the beginning
of the Spring 2006 semester. Specifically, we are looking for a student
to work on the symbolic part of the project and a student to work on the
neuromorphic part, as explained below.

A symbolic student must have a strong background in computer science,
plus a strong interest and an ambition toward creating a model of the
human mind. The task will be to design and to implement the core
architecture, while testing its conceptual framework on selected
practically interesting paradigms, and to integrate it with the
neuromorphic component. Specific background and experience in one of the
following areas is desirable: (1) cognitive architectures / intelligent
agent design; (2) computational linguistics / natural language
understanding; (3) hacking / phishing / network intrusion detection; (4)
advanced robotics / computer-human interface.

A neuromorphic candidate is expected to have a minimal background in one
of the following three fields. (1) Modern cognitive neuropsychology,
including, in particular, episodic and semantic memory, theory-of-mind,
the self and emotion studies, familiarity with functional neuroanatomy,
functional brain imaging data, cognitive-psychological models of memory
and attention. (2) Behavioral / system-level / computational
neuroscience. (3) Attractor neural network theory and computational
modeling. With a background in one of the fields, the student must be
willing to learn the other two fields, as the task will be to put them
together in a neuromorphic hybrid architecture design (that will also
include the symbolic core) and to map the result onto the human brain.

Not to mention that all candidates are expected to be interested in the
modern problem of consciousness, willing to learn new paradigms of
research, and committed to success of the team. Given the circumstances,
however, we do not expect all conditions listed above to be met. Our
minimal criterion is the excitement and the desire of an applicant to
build an artificial mind. I should add that this bold and seemingly
risky project provides a unique in the world opportunity to engage with
emergent, revolutionary activity that may change our lives.

Cordially,
Alexei Samsonovich

--
Alexei V Samsonovich, Ph.D.
George Mason University at Fairfax VA
703-993-4385 (o), 703-447-8032 (c)
Alexei V Samsonovich web site

Lies, Damned Lies, Statistics, and Probability of Abiogenesis Calculations

Abiogenesis - how the life self-formed.

Wired 13.08: The Birth of Google

Wired 13.08: The Birth of Google
It began with an argument. When he first met Larry Page in the summer of 1995, Sergey Brin was a second-year grad student in the computer science department at Stanford University.....

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.