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AI in Video Games vs. AI in Academia
missingmatterboy writes "Dr. Ian Lane Davis, AI researcher turned game development studio head, talks briefly about the differences between AI used in the game industry and the AI being researched in academic institutions. A short read but you may find it interesting."
Dragon Warrior 1 captured a girl's mind pretty well.
'Dost thou love me?'
'no'
'But thou must! Dost thou love me?'
'no'
'But thou must! Dost thou love me?'
*sigh*
'yes'
'I'm so happy!' *Cue music*
"I only speak the truth"
Karma: null(Mostly affected by an unassigned variable)
Make love, not sigs
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This seems a bit much even for Wired. The creatures in these games are following a predefined set of rules, certainly they are a complex set of rules, but the way they "learn" is entirely predetermined (that is, what they learn depends on what they are exposed to, but the formula for converting exposure into knowledge is set by the game designers). I think the fact that the graphics are rendered so realistically makes it easier to make the leap to thinking they are really acting "intelligent."
Who knows what really sets human intelligence apart, is it ability to make rules or nondeterministic memory or whatever, but it seems evident (to me, in my ever-so-humble opinion) that these creatures don't have it.
- adam
I submit that AI is already good enough to substitue for most human interactions.
Humpty Dumpty was pushed.
Most video games I've played had a pretty simple AI algorithm:
Easy - Computer player doesn't cheat
Medium - Computer cheats and always knows where you are or what you are doing
Hard - Computer cheats and is allowed to break the rules.
If game programmers spent more time writing smart (as opposed to cheating) computer opponents and less time trying to get 10 million more polygons on the screen, todays games might actually be worth buying.
On the other hand the game industry hasn't really used a lot of the research academia has come up with. It would be really cool to see some text-to-speech stuff in games. That would probably make the dialogue in games a whole lot better.
PK
This is a big debate in the AI community. They're devided into the "strong" and "weak" camps.
Strong AI says that it's entirely possible to make computer programs that think and feel just like humans. After all, all human thought is the result of chemical processes which obey the laws of nature and can thus be described algorithmically.
Weak AI says that it's impossible to ever create a computer program that really thinks and feels and loves and hates like a human. The best we can hope for is to simulate these thoughts to create a close approximation.
Of course no computer system out there today can recreate the complexity of the human brain.
This is why I think Asimov's laws regarding intelligent robots/software should be implemented today.
There's a minor problem with that statement, which is that robots aren't nearly bright enough to do any of those things. Not yet, anyway.
For the robot to be able to preserve human life, it must first be able to recognize humans reliably; then it must have a sophisticated situational awareness to understand in what cases a human's life might be in danger, and further, it must be smart enough to understand in what ways that perilous situation might be averted.
For the robot to obey a human's command, it must first be able to accurately interpret that command. Speech and speaker recognition are getting better, but they aren't there yet. And for the robot to again have the situational awareness to know what it is doing and what the results of its actions will be (including whether they endanger a human, as above), it is going to need to be much smarter than anything you can point to today.
Just recognizing humans reliably is a problem. The situational awareness part won't be happening any time soon. Asimov's laws require robots to be a hell of a lot smarter than they are today. By the time robots are smart enough to actually do these things, I'm not sure we'll even care about Asimov's laws (an actual set of ethical values might be a good substitute; hell, it works on humans, somtimes, anyway).
"I don't think all those AI coders out there are thrilled by the idea that their lifes work is used for games
Maybe they're thrilled, maybe they aren't. Aside from conducting interviews with the researchers themselves, we really don't have any way of knowing. That's sort of beside the point, though.
I think the simple fact of the matter is that both applications probably benefit each other, although possibly not in the way most people might think. When I started out programming, a lot of my initial projects were focused on game development. A recurring theme in my thinking was ways to make the computer opponent "smarter", which naturally led me to wonder how I could make the computer learn new tactics and adapt to the human player's actions. As I quickly learned, adaptive systems research is serious stuff.
So, I decided to dig into whatever materials I could get my hands on related to artificial intelligence research and theory. To be honest, I never really got very far, but it remains an interest of mine to this day. I'd be willing to bet some of tomorrow's leading AI researchers are playing video games today. That seems like a pretty good benefit to me.
I guess the key point is this: if a particular application of a certain technology gets people excited about it, and interested in researching it, it's a Good Thing.
One of the main goals of AI in games is to make the computer do things that look like a reasonable person (not necessarily an opponent) would have done them. It doesn't matter if the underlying models are elegant or extensible or whatever. It just needs to make the game fun. But in academic AI, what matters is to get good models, good theory, etc. Academic AI is geared towards the long run. Game AI can be really simple -- for example, you could watch how 100 humans play the game, and try to encode their strategies into the computer player. That kind of "AI" would be uninteresting to academic researchers, but it could make for a fun game.
Probably a better question is what is AI? The term Artificial Intelligence spurs the imagination and has an almost mystical sound to it, but in reality there are a lot of (seemingly) simple things encompassed by the AI field.
Some examples everyone can relate to:
Real-time spell and grammar checks in MS Word with autocorrection.
Pathfinding: Mapquest uses it. Your cable-modem-router uses it too.
Fuzzy logic: An oven that hovers 1 or 2 degrees around the target temperature instead of going 5 degrees above the target and then shutting off until it falls 15 degrees below the target.
Trolls throughout history:
Jonathan Swift
Maybe he meant 2 * 10^14, which would at least only be 3 orders of magnitude off.
A much closer approximation is 100,000,000,000 neurons, and 5,000 times that many connections.
(For more on the number of neurons in the brain, see R.W. Williams and K. Herrup, Ann. Review Neuroscience, 11:423-453, 1988)
If a single neuron could perform the equivilant of an instruction, then human brains would only be 100-1000 times more powerful than a modern desktop computer, probably less when you consider that they're more like a beowolf cluster than a single powerful computer.
-- Spam Wolf, the best spam blocking vaporware yet!
When looking at AI and Cognitive research, you really have to keep in mind that there are two differwent motivations at work in doing the research.
One motivation - the one alluded to in the article - is to make stuff that gives the same behavior as humans (or whatever animal you are looking at). You don't really care whether your methods are biologially correct, you want things that work. Most of classical AI falls into this category.
The other motivation is to figure out how we do things (we being animals in general). If the research ends up being useful in appolications, great, but that's not the goal of the work. You really want models of how real brains solve problem, and these models may be far too inomplete or computationally intensive to be used in implementations, yet be perfectly fine for their intended use. A lot of Cognitive science falls into this category.
Game AI designers probably have a much richer mine of information and techniques in AI than in cognitive research, and they have so far been able to exploit that knowledge - as well as judicious 'cheating' - to make a compelling illusion. If/when they turn to cognitive science, however, the pickings will be slimmer and harder to use, as the methods and models aren't designed to solve any kind of real-world problems to begin with.
/Janne
Trust the Computer. The Computer is your friend.
Now that we've been told, yet again, how limited AI is can we take away their moderator privledges? The AIs keep moding me offtopic...damn metaphorically challenged silicone... Oh no here they come again...
heuristic algorithm seeks stochastic relationship
This posting quotes from the book to make this point.
Most households were first introduced to computers by video games. It does not surprise me that the first introduction to AI for many people is computer games. I realize that spell checking and grammer checking, a form of AI, may be in many houses too.
Even the military is using game-developed technology for combat simulators.
Vintage computer games and RPG books available. Email me if you're interested.
It was a pleasure for me, as an AI prof. who does games-related research, to read this interview. IMHO Dr. Davis gave a brief but extremely accurate and informative sketch of the relationship between industrial AI and AI research. I wish that every "expert" publically commenting about AI could be as insightful and honest.
For example, computer vision -- there are publicly-traded companies out there which have been doing machine vision for YEARS. These systems are used by all major chip manufacturers, most major paper and textile manufacturers, etc. to catch recognize and catch defects in products before they leave the assembly line. Cognex is a $1B a year company -- they exclusively do machine vision and visual pattern recognition for industrial applications.
Another example of a company applying AI would be Virage, who has several patents relating to image/video searching and indexing.
Many investment houses use neural networks to profile and model investments, and plenty of large financials use expert systems and neural networks to for data mining, employee profiling, and so on.
Expert systems have been applied to computer security as well -- Rapid 7 (my company) sells a network security scanner which uses the Jess expert system from Sandia labs. The value of the expert system is, it allows the product to use discovered vulnerabilities to further exploit the network, discovering more vulnerabilities, which enable more probes to be performed, etc.
I'm sick of people asking "When will we see widespread commercial application of AI". AI researchers often quote the so-called "moving frontier" problem, that is, as soon as an AI application becomes useful enough to solve real-world problems, it ceases to be known as AI and looks a whole lot more mundane.
Could it be because it was never AI to begin with? I am sick and tired of the GOFAI (good old fashioned AI) community pasting the AI label on every clever computer application out there so they can cover up their failure to come up with human-level AI. People are not stupid. They can tell the difference between automatic cruise control and HAL. The former is not AI, it's just a clever hack. The latter has real intelligence. Let's face it. The GOFAI research community has failed. They had no clue as to what intelligence is about when they started the field fifty years ago and they have no clue now. We need new blood and new ideas in AI research.
character recognition software that reads zipcodes in the post office
natural language translation from french to english
diagnosis and treatment of disease
datamining
texture synthesis
making a helicopter hover still in the air
Robotics is interesting in that it is the holistic (Rod Brooks) view of AI: a robot needs sensory systems, control systems, a planner, etc.
Wow. All the brilliant people at MIT and a dozen other world-class research institutions have been plugging away at this problem, and you managed to figure it all out after a couple of semesters of Lisp. Bravo. Well done. When it's time to accept your Nobel Prize for this remarkable insight, I hope you won't embarrass all the other new laureates by pointing out that all their research was bunk as well. They'd be crushed.
Neurons do not "learn" information in any deep, metaphysical, cogito ergo sum sense. They simply grow and develop based on the inputs they receive.
Is this one of those, "Well, duh" points? Of course it is. You realize this fact as well as I do. But you ignore its implications. There's nothing impossible about creating a software-based "neuron" that can receive inputs, alter itself in response, and then propagate signals to other neurons. Such a construct would be too complex for a programmer to maintain on anything but the highest levels. Therefore, it could not be described merely as a mundane codification of the programmer's intelligence.
The biochemical processes by which intelligence arises in humans, however complicated, are irrelevant in theory. Computing is going on inside your skull, and a Turing machine can properly perform any computation devisable. I believe it's only a matter of time.
Despite what your many many weeks of Lisp programming might have taught you, AI already exists in many forms. They're already doing things thought to be solely the purview of wetware as little as a decade ago. I think the situation within AI right now is analogous to biochemistry back when vitalism was in vogue (18th century, IIRC). Everyone thought that there was something unique and downright supernatural about the chemistry of life. It was even said that no organic molecule would ever be synthesized in a laboratory. Then someone synthesized a really simple molecule--possibly formic acid. Eventually, Watson and Crick came along, and these days nobody in the field would entertain the claim that something in biochemistry can never be understood in principle.
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PhysicsGenius. Heh. Troll handle if I ever heard one.
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