
To create a super-intelligent machine, start with an equation - kr4
http://theconversation.com/to-create-a-super-intelligent-machine-start-with-an-equation-20756
======
gliese1337
It's interesting that, while the system can learn lots of different games, you
still have to give it the reward function special for each different game.
This may seem obvious, and not much of a limitation- after all, lots of things
that we think of as intelligent to varying degrees (different human beings, as
well as members of other species) have wildly different ideas of what
constitutes "reward", so that can't be an inherent part of the definition of
intelligence.

But you don't have to explicitly tell a human what the reward function for
Pac-Man is. Show a human the game and they'll figure it out. Which makes me
wonder if, while there is some room for variability in reward functions, there
might be some basic underlying reward computation that _is_ inherent in
intelligence. I can't find the link just now, but I read an article a few
months ago (might've even been here on HN) about a system that demonstrated
the appearance of intelligent behavior by trying to minimize entropy and
maximize its possible choices for as long as possible within a given world
model.

~~~
humbledrone
Pac-Man was designed as a game for humans, with a priori knowledge of what
kinds of things humans find rewarding. Thus the goal is obvious because it was
designed to be similar to other human goals. Eat the food, don't get eaten.
For this reason, it's not at all special that humans can determine the goal of
the game.

~~~
chongli
Yeah. Try sticking a more abstract game like Go in front of a random person
and see how that works out. Without being taught the rules, a human will have
absolutely no idea how to proceed. This would put a human in pretty much the
same boat as a computer.

~~~
dwaltrip
Secure the largest amount of territory and capture enemy groups? Seems pretty
human :p

~~~
jkarni
Not to Edward Lasker: "The rules of go are so elegant, organic and rigorously
logical that if intelligent life forms exist elsewhere in the universe they
almost certainly play go."

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melloclello
> AIXI now uses this model for approximately predicting the future and bases
> its decisions on these tentative forecasts. AIXI contemplates possible
> future behaviour: “If I do this action, followed by that action, etc, this
> or that will (un)likely happen, which could be good or bad. And if I do this
> other action sequence, it may be better or worse.”

What I want to know, is how long does it spend doing this? Does the duration
of one of these cycles have to be manually chosen, or is it smart enough/self-
aware enough to realise, okay, if I spend forever thinking about something,
then nothing will happen?

I guess what I'm asking is, is the algorithm smart enough to eventually
internalise its own model of itself?

~~~
adamwong246
That's a really good question because a sense of self is integral to our
definition of consciousness. Presumably this machine as able to understand
math, so it should be able to comprehend it's own formula or source code.

------
gjm11
Blogspam. Original source: [http://theconversation.com/to-create-a-super-
intelligent-mac...](http://theconversation.com/to-create-a-super-intelligent-
machine-start-with-an-equation-20756)

(If you happen to have encountered Marcus Hutter's AIXI before, there is
nothing new there compared with any other presentation of his ideas.)

~~~
ColinWright
Looks like the mods have changed the URL to the original source.

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JulianMorrison
Am I the only one that gets a bit of a chill watching an UFAI play a game
where it runs from its pursuers collecting resources until it gains the power
to kill them?

~~~
hcarvalhoalves
To be sincere, I don't know what kind of Pac Man implementation was that. In
the original game, each ghost has a personality [1], the ones in the video
seem to just wander aimlessly, so it doesn't really tell much about strength
of the algorithm to beat the game because it's not the same game.

So, to comment on your dystopian prediction, I'm not sure this algorithm would
do any better against humans, unless all we do is wander aimlessly too ;)

[1]
[http://www.webpacman.com/ghosts.html](http://www.webpacman.com/ghosts.html)

~~~
bradbeattie
More detailed information on how the ghosts behave:
[http://home.comcast.net/~jpittman2/pacman/pacmandossier.html...](http://home.comcast.net/~jpittman2/pacman/pacmandossier.html#Chapter_4)

------
ccozan
I highly recommend Markus Hutter's book: "Universal Artificial Intelligence:
Sequential Decisions Based On Algorithmic Probability"
([http://www.amazon.com/Universal-Artificial-Intelligence-
Algo...](http://www.amazon.com/Universal-Artificial-Intelligence-Algorithmic-
Probability/dp/3540221395/ref=la_B001JOUJH0_1_1) )

Beware, you need some serious maths and a board to follow him. In the end the
book is quite rewarding.

Now to AIXI: I don't belive that having a formula solves AI problem. Yes you
can model it, but in real environment, I believe there are simpler models to
try first. Remember: nature like simpliness first, complex when needed.

------
AndrewKemendo
>This scientific field is called universal artificial intelligence

How many times do we need to re-define the same concept? Artificial
Intelligence, Human Level Artificial Intelligence, Artificial General
Intelligence, Strong Artificial Intelligence etc....

Lets pick one as a community and stick with it. I thought AGI was in the lead
there recently what with the conference, journal and high amount of web
searches but apparently that isn't enough.

Beyond that, I would like to see how they make their algorithm narrow for the
narrow application of the Pac-Man game while keeping it generalizable. My
guess is that they don't and this algo ends up being a "starting point" for
narrow AI applications to rest on. In that case it's fine and interesting, but
doesn't pass the test to integrating specificity within a general learning
model.

~~~
maaku
> How many times do we need to re-define the same concept? Artificial
> Intelligence, Human Level Artificial Intelligence, Artificial General
> Intelligence, Strong Artificial Intelligence etc....

Every time the name gets co-opted to mean something else. These days
"artificial intelligence" == "machine learning and natural language
processing" which is most definitely _not_ what TFA is about.

~~~
SeanLuke
No. There is a perfectly good term which has been around for a very long time:
Strong AI. In no context does this mean "ML + NLP" (which, by the way, AI
itself hardly means). I think terms like AGI are just rebranding.

~~~
maaku
I don't think strong AI has quite the history you think it does. It's one of
these later coinages. Also, it has separate quasi-related meanings in
philosophy of the mind...

~~~
JohnHaugeland
Strong AI is one of those hand-wavey things that makes you sound like you're
talking about something well defined, when you actually aren't. Has always
struck me as counter-productive.

Frustrating stuff.

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valtron
Like others have said, this seems to be a generalization of the problem of
reinforcement learning. For a good introduction to the subject, check out
Reinforcement Learning by Sutton & Barto [1]. After reading the first few
chapters, you'll be able to understand most of that equation.

[1] [http://webdocs.cs.ualberta.ca/~sutton/book/the-
book.html](http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html)

~~~
xerophtye
Yeah just looks like Q-Learning to me :/

You just use the rewards to optimize the function that tells you what's the
predicted reward at any stage for a given action. And then take those best
acions

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yetanotherphd
For those having trouble getting past the hype to the actual content of AIXI,
lesswrong has a nice article [1]. From the article:

"The AIXI formalism says roughly to consider all possible computable models of
the environment, Bayes-update them on past experiences, and use the resulting
updated predictions to model the expected sensory reward of all possible
strategies."

I will add that the prior over models comes from a measure of model
complexity, which is a reasonable way to construct a prior.

While I think the idea of connecting deep mathematical formalisms with actual
AI goals is mostly hype, these formalisms are still interesting in themselves.
There is something very intuitive about how AIXI works, but that doesn't mean
it is going to practical for a large range of problems.

[1] [http://wiki.lesswrong.com/wiki/AIXI](http://wiki.lesswrong.com/wiki/AIXI)

~~~
dinkumthinkum
I don't recommend reading anything on that website. That way madness lies.

~~~
yetanotherphd
I don't read lesswrong, but this article is worthy on its own merits. No
memetic hazards are lurking in that particular article :-)

------
johnohara
These are good generalized life lessons for humans as well.

Seek new knowledge, learn well, reason logically and inductively, recognize
patterns and generalize, contemplate and plan for the future, converse and be
social, survive, and strive for optimum health (my addition).

Very good advice in whether or not you're an automaton.

~~~
Houshalter
Those words are pretty much meaningless. Of course everyone is already trying
to survive and recognize patterns and all that.

------
welldefined
1\. Do we have to engineer how much it's being rewarded in each game?

2\. What happens in the infinite case? With pacman, there are always a finite
number of choices at each state.

3\. If it doesn't have to be told the rules, then how does it make decisions?
In theory, it may be able to learn how to play jeopardy, but it may be way too
inefficient in practice. Humans don't even start with a blank state.

4\. I don't think I ever consciously apply philosophical principles like
ockham's razor when I problem solve or learn. It makes me a little
uncomfortable that we're starting with a philosophy, rather than having the
system discover things itself. I would be ok with it if there was some
parallel between ockham's razor and physics (not the methodology of science).

~~~
maaku
> I don't think I ever consciously apply philosophical principles like
> ockham's razor when I problem solve or learn. It makes me a little
> uncomfortable that we're starting with a philosophy, rather than having the
> system discover things itself. I would be ok with it if there was some
> parallel between ockham's razor and physics.

Why do you think it is relevant what you do consciously? The only things that
you do consciously are those things which your brain is ill-equipped to do.
The vast majority of your thinking processes are subconscious, as are the
principles that drive your conscious thinking. And I guarantee you, Ockham's
razor is in there whether you realize it or not. When things get complicated
do you purposefully look for a simpler solution? When trying to understand an
unknown situation, do you start with something simple and ad complexity as
needed? Ockham's razor.

> I would be ok with it if there was some parallel between ockham's razor and
> physics.

... there's not?

EDIT: As an AI researcher, I'd be more interested in creating an artificial
scientist than "artificial science." So what makes the scientist work?
Ockham's razor is at the foundation of that.

------
EGreg
YEs this is all good but who determines whether something is "good" or "bad"?
That's the interesting part. Who sets the goals? And how do they score an
intermediate situation on the way to achieving them?

Don't get me wrong, a machine can achieve goals is really, really useful.
After all, chess playing programs use the AlphaBeta algorithm to prune future
positions intelligently, but to score the positions they still need human
input. It may be, however, that they infer their OWN rules from past
positions, with absolutely no human input. Then it becomes interesting. Still,
the initial rules of the chess game have to be set down.

So while this is intelligence, this is not sentience.

~~~
Houshalter
I assume it gets reward for getting points in the pacman game (eating dots and
ghosts) and presumably loses them if it dies or loses the game. There might
also be a time factor involved so it doesn't waste time.

I'm really not entirely sure how it's decision making process works and I'm
really curious to know. Because simulating every possibility would be
ridiculous, but trying to predict the distant future based on some small
action that happens in the present is also really difficult.

>So while this is intelligence, this is not sentience.

No one is claiming it is, and "sentience" is a really dubious concept itself.

------
darkxanthos
This article is powerful. The idea of working to blend our philisophical
theorems into mathematical modeling is brilliant and should be obvious.

Their definition of intelligence is also extremely precise and gives me a new
way to look at the world.

------
Rangi42
Is AIXI capable of recognizing itself? If an AIXI agent were controlling a
robot body, would it realize that the body's actions correlate with its own
intentions? Would it pass the mirror test?

~~~
maaku
AIXI is capable of anything. Give it a utility function (and a pocket universe
of computium to run itself) and it will learn behavior that maximizes that
utility.

That said, things like the mirror test are so wrapped up in the construction
of our own brain that it really doesn't make sense to apply alien
intelligences, as you'd you'd be unlikely to get a visible response for
unrelated reasons. "That thing in the mirror is me, so what?" Actually that's
a bit misleading - the AIXI will have no internal dialogue. It's not built
that way. But give it _any_ task, and it will learn to do that task. If that
task is to recognize itself in mirrors, it will... but it will do so in a way
that is markedly different from humans.

------
j2kun
Does anyone know of an overview for people who already know the relevant
mathematics? Does their "English" definition have a corresponding mathematical
definition?

------
jkbyc
How is this better than applying the minimum description length principle
formalization of Occam's razor? (from 1978 [1])

I admit that I didn't spend time to really delve into it but nothing in this
article strikes me as particularly ground breaking.

[1]
[http://en.wikipedia.org/wiki/Minimum_description_length](http://en.wikipedia.org/wiki/Minimum_description_length)

edit: innovative --> ground breaking

~~~
maaku
It's basically that, formulated into a reinforcement learning algorithm.

It's innovative because this combination which is unique to Marcus Hutter's
Ph.D. thesis basically "solves" the problem of general AI.. if given infinite
computation resources. It's provable mathmatically to the be the best possible
artificial general intelligence, with constant-time operation.. it's just that
constant happens to be larger than the known universe.

That sounds useless, but it's not. What is shows is that artificial (general)
intelligence is really an optimization problem, and provides a (theoretical)
benchmark comparison, and directs current research into more practical
algorithms which approximate that ideal within realistic space and time
constraints.

~~~
jmmcd
It's not constant-time. It's not even computable, even in theory, even with
infinite resources.

~~~
Houshalter
True but the approximations of it are (i.e. limiting the amount of running
time each hypothesis has and limiting the number of hypothesis you test.)

This is sort of like criticizing the concept of a Turing machine because no
one has built one with infinite tape or running time.

~~~
maaku
Further, you can show with information theory that limiting the number of
hypothesis tested or the running time does not reduce generality so long as
you test up to a certain amount for a problem domain (e.g. hypothesis sizes up
to the maximum representable if the causally observable universe where
converted into computium, and maximum number of steps equal to the number of
possible combinatorial configurations of program state).

Of course as mentioned, this gives you constant factors equal to the size of
the observable universe. No one said it was practical :)

~~~
jmmcd
"Constant time" is still wrong even in the approximations. Constant time means
O(1). But your comment already refers to a running-time complexity dependence
on several parameters of the input, such as the size of the observable
universe.

~~~
maaku
Size of the observable universe is constant. And maximization by exhaustive
search requires touching every possible state. So yes, it is O(1). This is
explained in Marcus' Ph.D. thesis, I believe.

~~~
jmmcd
If he says so, I should admit I'm wrong, but maybe not just yet.

This way of thinking (size of the observable universe is constant) avoids the
whole question of time complexity. To actually measure the time complexity of
the algorithm, you need to consider inputs of different sizes. You could run
exactly the same algorithm in a different universe (that is why it's called
"universal" after all) and you'd get a different running time. The time
complexity is then the relationship between the two running times and the
universe sizes.

------
adamwong246
What if you gradually introduce more and more complicated games? Start with
Pong, then PacMan, Super Mario World, etc etc until you get to today's games,
which are far more complicated and more importantly, very realistic. Now that
you AI is well trained, attach a camera to the computer and point the machine
at the most complicated game of all: reality.

~~~
jmmcd
Good point: I don't think they've ever taken an agent which has been learning
for a while in one environment and placed it in another. I don't know, but I
would expect that the current version at least would have to fail hard,
basically unlearning everything, before it started to succeed in the new
environment. That's a weakness, if so.

How do humans avoid this? Because our brains are evolved to be plastic but not
too plastic. We encounter new environments (like going from one level of Pac-
Man to the next), but not new laws of physics (like going from Pac-Man to
Mario).

~~~
adamwong246
The whole point is that the intelligence is "universal." By definition, we are
talking about a thing which is able to transcend what it knows. So to me, the
whole point is to introduce a variety of environments so that it learns the
truly salient knowledge that is applicable across many games. And the most
important of facts is this: stay alive. We might be able to bootstrap an ai up
to a self-aware, self-preserving, rational agent which can adapt to any
environment.

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cheeko1234
Can someone explain the equation in this article?

~~~
rwallace
Yes. The core of AIXI is the part that figures out the nature of the universe
in which it finds itself (a formalization of induction), and the algorithm for
that is essentially "simulate all possible universes and see which ones give
data that matches our observations, then apply Occam's razor and assume the
simplest one is true".

It will not have escaped your notice that "simulate all possible universes"
would require an infinitely powerful computer, and even finite approximations
quickly become wildly intractable, making the algorithm unusable for anything
significantly more complex than Pac-Man. Thus AIXI is interesting for
philosophical purposes, being a mathematical formalization of intelligence,
but not useful for engineering purposes.

------
jk4930
Here's a 5 min video of AIXI playing Pacman (the description provides
details):
[https://www.youtube.com/watch?v=RhQTWidQQ8U](https://www.youtube.com/watch?v=RhQTWidQQ8U)

Look for YT videos with Marcus Hutter, they're excellent. He explains very
good, very systematic. Great stuff.

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j2kun
I'm surprised there's no reference to computational complexity. I can give a
perfectly fine example of a machine that satisfies their definition but would
not be considered intelligent because it's inefficient.

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loceng
It certainly makes sense to have a number of if-checks, which an equation
essentially is. The difficult part is feeding the equation with the correct
information, and with the correct timing and sequence, etc..

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kaa2102
What if we tried to mirror the evolutionary process by starting with an
objective like survive, reproduce, etc?

~~~
klipt
That's the idea genetic algorithms, genetic programming etc. are based on.

------
swframe
Surely, it will be built faster if it is open source.

