Artificial General Intelligence

The most powerful intelligence on the planet right now consists of billions of human brains connected by an ever smarter internet. That is AGI. And it is already out of the box.

-- Matt Mahoney, 2011-03-02

We are moving to an economic model where information increasingly has negative value. Get used to it.

-- Matt Mahoney, 2011-03-18

I suggest that if you want to make money in AI, then work on narrow AI, because lots of specialists put together make AGI. AGI, as a vastly smarter internet,
will be too big for anyone to own, so you shouldn't even try.

-- Matt Mahoney, 2011-05-20

Cryonics scam

It's amazing how many smart people fall prey for the Cryonics scam.

Here're my reasons why cryonics is a scam.

1) The chances of successful revival are extremely slim.
The process of reviving frozen people was never tested.
That means that most likely something would almost definitely go wrong:
Either freezing process, or maintaining frozen body, or unfreezing.
Most likely failures would be in every step.
I'd say that the chances of successful revival of the dead body are well below one in one thousand.

2) The cost of maintaining frozen body for several hundred years is pretty high. The chance that frozen body would never be heated up to unacceptable temperature during these these hundred years is pretty low.
In fact such accidents have been reported already. We should assume that many more accidents like that were never reported, because it's not in the interests of Cryonics companies to report them.

3) Even if it would be possible to revive your frozen body -- what would be the motivation to unfreeze you? In 25th century it would be much more productive to clone genetically modified super-humans (or better yet -- silicon AGIs) than revive hardly functional brain of person who was frozen with terminal decease in 21st century.


What causes people to believe in Cryonics?
I guess it's the same reason that pushes people toward religion -- they're terrified by their own death.

The catch is that Cryonics makes people die even earlier than they would die otherwise.

Enjoy Penn and Teller take on Cryonics:


Cryonics competes for people's money on the same level as any other religions do. I think that eventually Cryonics will be fully transformed into religion (like it happened with Scientology).
Scientology and Cryonics might even merge with each other
:-)

The only weakness of Artificial Intelligent Systems

~4 years ago I wrote small article about Weaknesses of Artificial Intelligent System.
That article listed only one weakness: artificial system didn't pass natural selection, while human evolution did.
I think I should clarify what exactly this "no evolution" weakness mean.

Natural selection = millions of years of testing

I'm looking at AIS (Artificial Intelligent System) from engineering perspective. All systems need to be tested, and all discovered problems need to be fixed.
Humans have billions years of testing and fixing bugs.
Artificial Intelligent Systems wouldn't have such luxury.
That would mean that some obscure (but important) design problems most likely won't be found, and under certain circumstances these design problems may hurt AI System or even significantly damage the whole society of AI Systems.

Natural Selection and Emotions

Noel Anthony Pierre in his article Social Considerations for Artificial Intelligence assumes that artificially crafted Intelligent System would rely on logic only and wouldn't use emotions.
That's not correct. Logic cannot work without low level intelligent support that emotions provide. That's why Artificial Intelligent Systems would have emotions.
However because of limited testing period (years of testing by engineers versus millions of years of testing by Evolution), artificially crafted emotions wouldn't be as carefully tuned as human emotions.

See also:
More discussions about AI weaknesses

Narrow AI in PostJobFree.com

I strongly believe that the best way to AGI (Artificial General Intelligence) is building narrow AI and then gradually extend it toward more and more General Intelligence.

Finally, I implemented some of my AI techniques in real-life web site PostJobFree.com.
Now PostJobFree.com intelligently calculates Daily Job Posting Limit. The calculations are based on how many times recruiter's postings were viewed, and how many times these postings were reported as spam.
I cannot claim that this feature has "advanced intelligence", but it is intelligent nevertheless.

Here are intelligent techniques we used to build that feature:

1) Preprocessing data prior to using it in decision making.
Row data is coming in the form of "page views" and "spam report clicks".
Special process raw input into RecruiterRating and JobRating tables.

2) Forgetting.
The most recent data is usually more valuable for decision making.
That's why yet another PostJobFree process makes sure that old data is slowly losing it's value (and disappears if the value is too low).
We implemented it by simply decreasing values in some columns in RecruiterRating and JobRating tables by 1% every night.


Here's what I've learned from implementing my first real-life intelligent feature:
1) The best working formulas and algorithms are relatively simple.
2) Still it takes time to carefully propose, test, chose, and implement intelligent algorithm.
3) If the system is designed properly - performance is not an issue.

Artificial General Intelligence project

Funny quote from AGI mailing list:

=======
Vladimir Nesov wrote:
> On Tue, Mar 11, 2008 at 7:20 AM, Linas Vepstas wrote:

Linas Vepstas: How about joining effort with one of the existing AGI projects?

Vladimir Nesov: "They are all hopeless, of course. That's what every AGI researcher
will tell you... ;-)"

Richard Loosemore: "Oh no: what every AGI researcher will tell you is that every project is hopeless EXCEPT one. ;-)"
=======

How do we learn

Mark Gluck gives an interesting explanation about cognitive processes in human brain:
The Cognitive and Computational Neuroscience...

Mark explains that we learn both from observation and from experiment.

Reducing AGI complexity: copy only high level brain design

In my previous post Complexity and incremental AGI design I claim that complexity has very serious impact on AGI development.
If we want to improve our chances of successful AGI implementation, we need to cut complexity as much as possible.
In this post I want to touch the topic of copying human brain design while developing AGI.
Human brain structure is very complex it's almost impossible to describe in details how exactly brain works.
Richard Loosemore explains why this is the case:

Imagine that we got a bunch of computers and connected them with a network that allowed each one to talk to (say) the ten nearest machines.

Imagine that each one is running a very simple program: it keeps a handful of local parameters (U, V, W, X, Y) and it updates the values of its own parameters according to what the neighboring machines are doing with their parameters.

How does it do the updating? Well, imagine some really messy and bizarre algorithm that involves looking at the neighbors' values, then using them to cross reference each other, and introduce delays and gradients and stuff.

On the face of it, you might think that the result will be that the U V W X Y values just show a random sequence of fluctuations.

Well, we know two things about such a system.

1) Experience tells us that even though some systems like that are just random mush, there are some (a noticeably large number in fact) that have overall behavior that shows 'regularities'. For example, much to our surprise we might see waves in the U values. And every time two waves hit each other, a vortex is created for exactly 20 minutes, then it stops. I am making this up, but that is the kind of thing that could happen.

2) The algorithm is so messy that we cannot do any math to analyze and predict the behavior of the system. All we can do is say that we have absolutely no techniques that will allow us to mathematical progress on the problem today, and we do not know if at ANY time in future history there will be a mathematics that will cope with this system.

What this means is that the waves and vortices we observed cannot be "explained" in the normal way. We see them happening, but we do not know why they do. The bizarre algorithm is the "low level mechanism" and the waves and vortices are the "high level behavior", and when I say there is a "Global-Local Disconnect" in this system, all I mean is that we are completely stuck when it comes to explaining the high level in terms of the low level.

Believe me, it is childishly easy to write down equations/algorithms for a system like this that are so profoundly intractable that no mathematician would even think of touching them. You have to trust me on this. Call your local Math department at Harvard or somewhere, and check with them if you like.

As soon as the equations involve funky little dependencies such as:

"Pick two neighbors at random, then pick two parameters at random from each of these, and for the next day try to make one of my parameters (chosen at random, again) follow the average of those two as they were exactly 20 minutes ago, EXCEPT when neighbors 5 and 7 both show the same value of the V parameter, in which case drop this algorithm for the rest of the day and instead follow the substitute algorithm B...."

Now, this set of computers would be a wicked example of a complex system, even while the biggest supercomputer in the world, following a nice, well behaved algorithm, would not be complex at all.

The summary of this is as follows: there are some systems in which the interaction of the components are such that we must effectively declare that NO THEORY exists that would enable us to predict certain global regularities observed in these systems.

So, if low level brain design is incredibly complex - how do we copy it?

The answer is: "we don't copy low level brain design".
Low level design is not critical for AGI. Instead we observe high level brain patterns and try to implement them on top of our own, more understandable, low level design.

Complexity and incremental AGI design

Why is it so hard to build Artificial General Intelligence (AGI)?
It seems we have almost everything we need: great hardware, mature software development industry, Internet, Google, lots of successful narrow AI project ... but AGI is still to hard to crack.

The major reason is -- overall complexity of building AGI.

Richard Loosemore is writing about it:

Do we suspect that complexity is involved in intelligence? I could present lots of reasoning here, but instead I will resort to quoting Ben Goertzel: "There is no doubt that complexity, in the sense typically used in dynamical-systems-theory, presents a major issue for AGI systems"
Can I take it as understood that this is accepted, and move on?
So, yes, there is evidence that complexity is involved.

Richard also explains, how exactly complexity affects system development:

when you examine the way that complexity has an effect on systems, you find that it can have very quiet, subtle effects that do not jump right out at you and say "HERE I AM!", but they just lurk in the background and make it quietly impossible for you to get the system up above a certain level of functioning. To be more specific: when you really allow the symbol-building mechanisms, and the learning mechanisms, and the inference-control mechanisms to do their thing in a full scale system, the effects of tiny bits of complexity in the underlying design CAN have a huge impact. One particular design choice, for example, could mean the difference between a system that looks like it ought to work, but when you set it running autonomously it gradually drifts into imbecility without there being any clear reason.

The is a good technique of dealing with complex system -- increase complexity gradually and carefully test every step.
That's why I think it's so important to build testable narrow AI systems prior to building AGI.
We have many Narrow Artificial Intelligent Systems already, but we need more. And we need them to become more advanced up to the point when they become AGI.

Self-emergence of intelligence in humans and artificial systems

Human brain is self-emergent on many levels. Here's simplified sequence of human brain self emergence:
1) Human genes build "Brain Builder". Brain Builder consists of:
- Neurons Factory – neurons with reproductive ability.
- Brain Structure Manager – hormones and other mechanisms that define brain structure.

2) Brain builder builds "Empty Brain" --- fully assembled, but mostly empty brain: super goals are defined, but there is no external knowledge yet, no sub-goals defined yet.

3) By experimenting and learning Empty Brain evolves into Brain with Mind (fully working intelligent system, with lots of external knowledge and sub goals).

Every step in this sequence means self-emergence.

What do you think, when we build artificial intelligent system, what system should we build: Genes, Brain Builder, Empty Brain, or Brain with Mind?

I believe that building Empty Brain is our best option.
Below are my reasons.

Why not build Brain with Mind?

In order to build Brain with Mind we have to build Empty Brain anyway, but our task will be considerably more complex, because fully loaded mind is at least 10 times more complex than Empty Brain. It's like complexity of empty computer in comparison with complexity of all software which is loaded into regular "in use" computer.
Bottom line: there is no point to ai developers to pre-load mind into strong AI, when Empty Brain system can do it itself.

Why not build Brain Builder?

Complexity of Brain Builder is probably comparable with complexity of Empty Brain. But from engineering perspective developing Brain Builder is considerably more complex.
1) Let assume that we didn’t have designed Empty Brain yet. In this case we have no clue what the output of our Brain Builder should be. That means that we cannot test or debug Brain Builder. There are no checkpoints to verify that our development is on the right track.
Inability to test and debug complex system makes development of such system virtually impossible.
The only working approach in this situation would be to try to tweak some Brain Builder’s settings and then run full test: build Empty Brain and wait for several years to check if it evolves into Brain with Mind.
Mother Nature was quite efficient in this approach. It took just few billions years to develop proper Brain with Mind. I doubt that human researchers applying such approach would accomplish the task considerably faster.

2) Let assume that we already designed working model of Empty Brain. In this case what’s the point to design Brain Builder? Our industry can easily reproduce any working model in mass quantity.

Why not build Genes?

Building Genes which would build Brain Builder is even more complex than building Brain Builder itself.
The reasons are the same as in "Why not build Brain Builder?"
If we don’t have working model of Brain Builder yet – then we effectively cannot test & debug genes.
If we have working model of Brain Builder – then why bother with Genes?

Parallels with existing systems

1) CYC is trying to build Brain with Mind system. Actually even worse – they are trying to build Mind without Brain --- no self-learning ability, no super-goals.
That road leads nowhere.

2) Google is Brain with Mind which was developed as Empty Brain. Google's Empty Brain has working crawler and other self-learning mechanisms. This approach proved to be very efficient, and eventually Google's Empty Brain emerged into Brain with Mind – very smart search system.

3) It seems that there are no famous Brain Builder projects. But I’m sure that some researchers do attempts to build "Brain Builder". So far – no success at all for the reasons I explained above.

Conclusion

Building Empty Brain capable of self-emerging into fully capable Brain with Mind -- is the most feasible engineering approach in strong AI development.


---
This post is a result of discussion with David Ashley. He is a proponent of "Brain Builder" approach.

Intelligence: inherited through genes or gained from environment?

Human Intelligence is acquired from environment, not encoded genes.
Genes provide framework, which allow to learn from environment. This framework is critical for intelligence, but does not provide intelligence by itself.

===== By Richard Loosemore (2007 April 05) in AGIRI forum =====
If we were aliens, trying to understand a bunch of chess-playing IBM supercomputers that we had just discovered on an expedition to Earth, we might start by noticing that they all had very similar gross wiring patterns, where "gross wiring" just means the power cables, bundles of wires inside each rack, and wires laid down as tracks on circuit boards.
But nothing inside the chips themselves, and none of the "soft" wiring that exists in code or memory.

Having mapped this stuff, we might be impressed by how very similar the
gross wiring pattern was between the different supercomputers that we discovered, and so we might conclude that our discovery represented a significant advance in our understanding of how the machines worked.

.....

That last bit -- the [powerful algorithms that interact with the environment] bit -- is what makes the difference between a baby that sits there drooling and probing for its mother's nipple, and an adult human being who can understand the complexities of the human cognitive system.

Anyone who thinks that that last bit is also encoded in the human genome has got a heck of a lot of work to do ...
=====

Larry Page talks about AI

=====
Google's Page urges scientists to market themselves
Google co-founder Larry Page has a theory: your DNA is about 600 megabytes compressed, making it smaller than any modern operating system like Linux or Windows.
.....
"We have some people at Google (who) are really trying to build artificial intelligence and to do it on a large scale," Page said to a packed Hilton ballroom of scientists. "It's not as far off as people think."
=====

I agree with Larry Page: human's DNA has relatively small size.
Besides, not all human DNA is in charge of the brain. I guess that something like 10% of the whole DNA is related to brain development.

I wrote about that over 3 years ago:
-----
The time has come The time has come to develop Strong Artificial Intelligence System
Strong AI project is quite complex software project. However even more complex systems were implemented in the past. Many software projects are more complex than human DNA (note that human DNA contains way more than just genocode for intelligence).
-----

Should Strong AI have its own goals?

Short answer: Yes and No.
Long answer: Strong AI can add and modify millions of softcoded goals. At the same time Strong AI shouldn't be able to change its own super goals.
Why?

Here are the reasons:

1) In its normal working cycle strong AI modifies softcoded goals in complience with embedded super goals. If strong AI has ability to modify super goals then strong AI will modify (or terminate) super goals instead of achieving these goals.
Example:
Without ability to modify super goal "survive", computer will try to protect itself, will think about power supply, safety and so on.
With ability to modify super goals computer would simply terminate goal "survive" and create goal "do nothing" instead just because it's the easiest goal to achieve. Such "do-nothing" goal would result in the death of this computer.

2) If Strong AI can change its super goals then Strong AI would work for itself instead of working for its creator. Strong AI's behavior would eventually become uncontrollable by AI creator / operator.

3) Ability to reprogram its own super goals makes computer behave like a drug addict.
Example:
Computer can create new super goal for itself: "listen to music" or "roll the dices" or "calculate PI number" or "do nothing". It would result in Strong AI doing useless stuff or simply doing nothing. Final point: uselessness for society and death.

Massive words/phrases database publishes by Google

Google research publishes their massive words/phrases database:
===
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.
===
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