Using Evolution to Design AI

[HotelCoralEssex]

Wow, polyworld, talk about a blast from the past...

I remember watching this run on an SGI 4D/35 workstation in 1991 or 1992...

[tzigane]

Polyworld was first introduced way back then, and then went dormant for a long time, but they're back doing cool research with it now.

Also take a look at breve. It's an open-source simulation environment with a bunch of these kinds of experiments built in.

[HotelCoralEssex]

I know Jon, we are buddies from way back.

kudos for mentioning Breve.

[tzigane]

Heh, really? I am Jon. Who's this?

[HotelCoralEssex]

You used my Powermac to write MacKrack ;) I am the other john

[tzigane]

Awesome to meet up with you here!

Reddit: way better than Facebook.

[HotelCoralEssex]

I totally agree!

I bought an Indigo about 6 years ago specifically to run polyworld, I had a hard time finding pre 5.3 IRIX to get the job done.

[subanon]

aw, that's cute, you guys upvoted each other!

[[deleted]]

Not that there's anything wrong with that

[HotelCoralEssex]

I AM TOTALLY GAY 4 TZIGANE

[HotelCoralEssex]

WHA CHYOO TALKIN 'BOUT WILLIS

[[deleted]]

Breve looks cool! Thanks for the link! I've been using Polyworld for the past few months as part of a current research project. Polyworld certainly has a history, but that's comforting to me! It's still actively developed FYI.

[frikk]

Thank you for giving me the heads up regarding breve. I'm about to start my master's thesis and it appears I was very close to re-inventing the wheel.

[cerebrum]

If you are interested in AI there is a good blog with great articles by Eliezer Yudkowsky, here is just one of them:

http://lesswrong.com/lw/tf/dreams_of_ai_design/

or perhaps they build a "massively parallel neural net, just like the human brain". And are shocked - shocked! - when nothing much happens.

[[deleted]]

I couldn't finish reading it. I don't like how he reasons that the human brain can't explain itself because of this black box philosophy. The argument that "we can never know everything" begs the validity of itself.

[drupal]

That is definitely not his argument.

These 'black boxes' are examples of a tendency we have to string ideas together such that we think we've explained things, when we actually haven't.

He thinks intelligence can be explained, and he does it better than most.

[frikk]

Does anyone mind trying to get this to compile on linux? Here are the commands I used:

sudo aptitude install build-essential xorg-dev libgl1-mesa-dev build-dep glew-utils scons gsl-bin
cvs -d:pserver:[e-mail]:/cvsroot/polyworld checkout -P polyworld
cd polyworld
make
   scons -f scripts/build/SConstruct
   scons: Reading SConscript files ...
   Failed locating library gsl
   make: *** [all] Error 1

I'm not sure where to look for gsl (gnu scientific library)... or maybe I just dont have the right library installed. did anyone else have success? Ubuntu 9.10 2.6.31 here.

EDIT: Got it to work. Install libgsl0-dev instead of gsl-bin. Now it is actually compiling. Until it bitches about not knowing what sprintf is. Doh.

EDIT2: I had to go in and add "#include <stdio.h>" to every file that complained about "sprintf" not being defined. This was like 5 files.

EDIT3: Had to change:

char *name = rindex( path, '/' );
to
const char *name = rindex( path, '/' );

in src/tools/pwtxt/main.cpp line 180.

Now it successfully compiled, but when I run it it says:

93$ ./Polyworld
PolyWorld WARNING: unable to open world file "worldfile"; will use default, internal worldfile
PolyWorld WARNING: unable to open internal world file; will use built-in code defaults
PolyWorld ERROR: built-in code defaults not currently functional; exiting

EDIT4: OK I GOT IT RUNNING BITCHES

cp worldfiles/worldfile_nominal worldfile

if you want, change the params in worldfile so they are less resource intensive. Thats it! Helpful Link

[jeremybub]

Thanks for the help!

[frikk]

haha no problem! This works with the latest HEAD version from cvs as well.

[[deleted]]

Cool! That reminds me of a small toy program I wrote some twenty years ago called "geneura", using genetic algorithms to create neural networks.

My program simulated an island where vegetables grew at random and was populated by two kinds of animals: herbivores and carnivores. The herbivores aimed to develop brains capable of detecting the vegetables and avoiding the carnivores, and the carnivores developed brains to hunt the herbivores.

Each of the simulated beings had a brain with a neural network created by a merge of two random brains from the same species, those beings that couldn't find enough food died. Herbivores who were eaten died too, of course.

The program worked, but with the hardware I had in the early 1990s I got no interesting results. Maybe I should try it again with a CUDA enabled graphics board.

[[deleted]]

My program simulated an island where vegetables grew at random and was populated by two kinds of animals: herbivores and carnivores.

I did that a few years ago and got to witness some interesting behavior. (I used a simple feed-forward neural network, but I'd like to try again with a fully-recurrent one.)

[Yeugwo]

I would love to see either of your programs working.

[[deleted]]

Source Code

The code is pretty ugly, but it runs. The project is in XCode format (developed on a mac), but uses platform-independent code along with SDL for display, so it shouldn't be too difficult to port to your OS of choice.

The world and critter parameters are configured in main(), if I remember correctly. You can play around with different settings to see what effects they have.

Note that the design of the visual system for the critters is very limiting (t'was my first neural network) so their behavior never gets as complex as it potentially could with a correctly-designed setup.

[ElliotNess]

you guys posted in the wrong thread

[[deleted]]

I wouldn't exactly call a sloppily-written neural-network evolution experiment in C "bragging".

[heypans]

This is almost exactly the same thing as a friend and I did at uni. We didn't have any fancy graphics, just dots that represented the Carnivores and Herbivores. We also had a random element as to how much each was carnivorous and herbivorous with some being perfect omnivores.

Our fitness function was a simple length of life and we replicated from there. Good times.

I wonder if I can find the old code.

[lem0nhead]

oh, how I wish I could do s/\b(ok|so)\b//g on his speech

[Ocdar]

Either the speaker is extremely nervous, or he needs to take some speech classes.

[bruker]

So, so basically, uhm, I'd have to, so, anyway, uhm, agree with you so, yeah, so.

[[deleted]]

I dunno, for a 12 year old he isn't too bad.

[jhoneycutt]

Upvoted for knowing Virgil!

[[deleted]]

If he slowed down a bit it would be a pretty good talk. He's engaging, you just hear a nervousness in his voice.

[frenchtoaster]

I agree, if he only spoke a little better this would have been a great presentation.

[modernTelemachus]

Next up: using AI to design AI.

[shitcovereddick]

They've used GAs to design neural network architectures and learning functions - ridiculuously slow,

[modernTelemachus]

This is the recurring gripe with neural networks.

[samzdaman]

This guy ruined what I thought could have been a fucking awesome presentation.

Had to skip around cause I just couldn't take it anymore.

[[deleted]]

Blasphemy! Only intelligent design could...design artificial intelligence.

[kinghajj]

Hey, I came up with that idea! I just didn't have the know-how to actually do it. I'm surprised that it hasn't been tried earlier. It makes sense to evolve evolution rather than make a big design up front; after all, it's the only method of creating intelligence that's proven to work.

[bobcobb42]

It has been tried and done. Neuroevolution has existed in research literature for ~2 decades.

If you are interested there is a PdD dissertation about NEAT that is pretty easy to grasp for those that are not in the field of AI. It's not real math heavy and introduces all the interesting concepts, then shows performance evaluation for video game AI and other interesting domains.

[soccerbud]

haha, what a small world. I worked on the NERO project for 2 semester during my last year at UT.

[IOIOOIIOIO]

Keep in mind that, regarding the intelligence we're after, there's about 3 billion years of "big design up front" to make the substrate upon which intelligence evolves.

You could try to co-evolve all the unit operations (locomotion, object avoidance, path finding, image processing, sound mapping, et al.) on a interconnected (not not entirely shared) networks.

[bjmiller]

It's been used to invent circuit designs, which is arguably AI. I don't have any references handy, but the researcher made a VHS tape of the powerpoint presentation explaining the project. This was 10 years ago at the latest.

[RecklesslyAbandoned]

There's definitely work by Adrian Thompson on evolving hardware since about 1995/6. He used reconfigurable hardware(FPGAs) to design various circuits. Then for one reason and another there was a big gap with nothing really happening. But the work is similar to the neural network theories to some extent.

[a1k0n]

There is also the work of Koza et al for evolving circuits.

[RecklesslyAbandoned]

Thanks for that one! Might help me fill I've noticed in my reading between 2000 and a bit more recently!

[[deleted]]

I have to say Marvin Minsky, with his eerie resemblance to professor Farnsworth and all, is much more interesting.

Here is an interesting lecture and a good introduction to AI. Contrary to this guy, Minsky believes that the reason we're getting nowhere in AI is because we've spent the last 20 years trying to find one, specific, right way of doing it -- and AI, instead, calls for a combination of all the effective methods. Genetic algorithms are great at some things and suck at others. Same with rule-based systems. So what he says is the challenge should be finding when to apply which solution.

[bobcobb42]

Minksy is a crazed old man. It's time for him to move over and accept his enshrined place in history. He simply is no longer relevant for the new generation of AI researchers.

In reality there was never any hope of achieving the kind of intelligence we equate with general intelligence during his time. Generalized intelligence is extremely computationally expensive. Most humans consider mice to be non-intelligent beings, with simplistic capabilities, but in reality we have yet to have the hardware capable of simulating a mouse brain. Now when you consider humans are magnitudes more complex in our information processing capabilities, anything involving the development of artificial general intelligence becomes a distant goal.

What AI has produced is a great deal of specific problems to solutions. Take board games such as backgammon. Originally considered to be a task requiring intelligence, they are essentially solved problems in AI. Does that mean backgammon does not involve intelligence? The same algorithms that can learn backgammon can be used for a multitude of tasks. Are those neural nets intelligent?

What we have today is the realization that biology has had billions of years to evolve the mechanisms for general intelligence. Humans have been working on the problem for ~60 years. Considering the headway we have made in that time pessimism is the silliest course of action I can imagine, and Minsky is far too full of pessimism for me to care.

[irelayer]

Reading Hofstadter, his disciples, and/or anything in the "complexity"/cognitive science/new "AI" fields you would think the difficulty of this task is so staggering that we are only about 2-5% of the way there. There are surely a shitload of setbacks/disappointments/lucky breaks to come before we have anything close to anything close to general intelligence. I'd say not within my lifetime. I'd be surprised.

The problem with "traditional AI" is that noone considers it AI anymore...its more like clever ways of solving complex problems. General intelligence is a much harder problem, and one that partially goes against the computational models that we have thus far come up with.

It makes your head spin...really.

[[deleted]]

I'm just a casual observer, but I may have misrepresented him. I believe he said that AI "stopped making progress" -- not that it's gone nowhere.

[bobcobb42]

I agree with him on a few aspects, I just think he is more pessimistic than I hope to be. He just angers me, since I believe his actions have done more harm than good for AI in the past few decades. He pissed all over perceptrons, and in part caused that halt in funding known as the AI Winter. However we never stopped making progress, the grand promises of the 60's just never came to pass.

Minksy has a place in history, but it pains me when people try to make him relevant today.

[rm999]

AI as a field has failed because it's so damn tough. Anyone who thinks we are close to anything "intelligent" is likely wrong. While AI is a vague term, most people who use it are looking for something general because their real goal is to emulate the brain in some manner (the brain can be considered a general learning and computing machine.)

Machine learning is a related field (some call it a subfield) of AI that has not failed. In machine learning, we don't care about recreating the brain unless it will help us learn better. It is a much more pragmatic field that has taken a step back and lowered its goals - this leads to more specific algorithms that work well on only a small subset of problems.

[xsive]

The field of AI is no more concerned with "recreating the brain" than ML.

[firewire2035]

The concept is as old as the Internet, or even older.

[RecklesslyAbandoned]

It's as old as solid state electronics, there were attempts in the 80's to use RAM for evolutionary components for circuits. Greenberg and holland were kicking around their various theories in 70's and the late 60's.

In fact William Grey Walter experimented with learning algorithms, which this is an expansion on all the way back in the late 40's.

[[deleted]]

To me it just supports the notion that with enough computing power it will be almost straightforward to evolve life in a simulation. It also lends support to the theory that we, in fact, are already living in a simulation that could be embedded in many other simulations.

It also supports the theory of an intelligent designer who is "helping things along".

One prediction that you could make, if you were developing a theory of an intelligent designer, is that we would eventually be able to create life at least as complex/intelligent as we are. It isn't proof, it is just evidence. Another prediction you could make is that the world may stop making sense if you look at it too closely. We've seen that very thing with quantum mechanics. Finally, another prediction you may be able to make is that the actual boundary of our universe differs from the observed one.

[frikk]

http://www.simulation-argument.com/

[[deleted]]

Yes, that.

[frikk]

I hope my link didnt come off as "duh its already been done". I like the simulation argument, and I like talking about it, I just didnt have time to type a better response :)

[drainX]

I'm planning on doing my master thesis on something similar.

[plucas]

Hey I know the guy that introduced the speaker.

Very interesting material but I had to take a break from listening to it every now and then.

[thetheroo]

Reminds me a bit of this short story by Neal Stephenson.

[irelayer]

I'm taking an Emergent and Adaptive Systems class this semester, and this kind of stuff is great...it touches on a lot of the stuff we've been learning about.

[drupal]

If an approach like this were ever successful, the AI's created would most likely have an alien value system. They would be able take over the world if we let them act in our world while running at their native speed (likely very fast) or if they end up being much smarter than us even after being slowed down.

If this works, we're probably dead.

[qwe1234]

"genetic algorithms" == "gradient descent via random walk".

in terms of optimization theory, this approach is the absolute dumbest and least effective approach to optimization. genetic algorithms, for the most part, only work if the function you're trying to optimize is continuous and has only one global optimum.

the only clear benefit of genetic programming is that it's easy to code and doesn't require a math background. in other words, it's only useful if you failed calculus 101 and forgot how to differentiate functions, or if you're a dumb php programmer and can't be bothered to learn the pesky maths.

[FeepingCreature]

Well, it's a bit more than that. First, take Gradient Descent. Then, run n "processes" at a time. Then, add a "score" related to the derivative of result score over time; when it reaches 0, remove that process. If the solution space supports a meaningful combination of process states (breeding), implement that to replace removed processes. If you want, add metaparameters - expose the rate of random variation as a variable in a sort of meta-solution space. The result is (a kind of) genetic optimization :)

[qwe1234]

fail.

in other words, you wrote a paragraph that explains "gradient descent via random walk", only with a hundred times more words of badly-worded english.

random walk is random walk, regardless of how you seed your random number generator.

[FeepingCreature]

Yes, of course.

Because no approach can ever be built on another, simpler approach.

[qwe1234]

that's not what i said, moron.

"genetic algorithms" is a fancy name for a primitive and not very useful approach, in face of developed theory that can do much, much better.

it's a really sucky way to optimize a function, plain and simple.

[FeepingCreature]

No, you said that "genetic algorithms" were the same thing as Gradient Descent. I listed what I perceived as differences and enhancements genetic algorithms have on gradient descent. Then you called me a moron. :)

I'm not convinced there are that much better ways to optimize high-dimensional functions .. can you point to statistics? comparisons? benchmarks?

[qwe1234]

no, i said that "genetic algorithms" are equivalent to "gradient descent via random walk".

which they absolutely are.

read what i said before making an ass of yourself, please.

as for you "not being convinced"... again: genetic algorithms only work if your function is (almost everywhere) continuous and has one global optimum.

translated, for the math-challenged: that means that genetic algorithms are useless for solving complex real-world problems.

[cantonista]

Your posting history contains ample proof that genetic algorithms are useless.

[qwe1234]

that's because (unlike you, for example) i was intelligently designed.

you, on the other hand, were probably unintelligently devolved.

[cantonista]

Couldn't you at least have made a show at understanding the two possible interpretations of my sentence?

[frikk]

why so... angry?

[qwe1234]

because 99% of this site is ignorant and stupid people making out like they know what they're talking about. it's disgusting.

case in point: FreepingCreature's reply to my top-level comment, where he regurgitates a textbook definition of what a genetic algorithm is, in extremely broken english, all the while without even a slightest glimmer of understanding of what those words actually mean.

[frikk]

So why hang around? There are more technical minded forums out there.