
Artificial General Intelligence: Now Is the Time  - bootload
http://www.kurzweilai.net/meme/frame.html?main=/articles/art0701.html
======
jey
All the academic AI researchers are focusing on boring narrow AI problems, and
nobody has the balls to really tackle the big problem[s] of Artificial General
Intelligence. The AI field promised a lot in its early days, and failed to
deliver, so now everyone just plays it safe -- it has become taboo within the
AI field to even discuss "human-level AI". Some people think it's not
possible, but the meat computer mounted on top of your torso is an existence
proof that it is possible to build an intelligent system out of matter.

Here's a good but long video that discusses AGI and why it's feasible, aimed
at a technical audience. Skip over the introductions at the beginning. Google
Video: <http://video.google.com/videoplay?docid=-821191370462819511> Higher
quality 1GB MPEG4: <http://www.archive.org/details/FutureSalon_02_2006>
Shorter video on "The Human Importance of the Intelligence Explosion":
<http://video.google.com/videoplay?docid=6114518772001796913> Eliezer
Yudkowsky also has some interesting papers at <http://yudkowsky.net>

Help advance AGI, donate to the Singularity Institute for Artificial
Intelligence! <http://www.singinst.org/challenge> Feel free to email me if
you're interested in chatting on these topics.

~~~
machine
I disagree with you on a lot of points. First, I don't think AI has failed to
deliver. There are a ton of things we can do now that we couldn't 30 years
ago. Just to name a couple, how about simultaneous localization and mapping
<http://web.mit.edu/16.412j/www/html/readings/Eliazar+Parr-ijcai-03.pdf,> and
face detection <http://citeseer.ist.psu.edu/viola01robust.html.>

I also disagree that AI researchers are working on boring narrow problems.
There are plenty of researchers working on huge open problems like object
recognition, speech recognition, reinforcement learning, etc. And they're
making great progress too. I think the reason most AI researchers don't talk
about making "human-level AI" is that this isn't really a quantifiable goal.
There are plenty of things computers can do better than people already (would
you want a computer with "human-level" addition and subtraction?). And for
just about every specific useful task humans do better than computers, there
are a few dozen AI researchers working on it. Why is that not a reasonable
approach to the problem?

~~~
jey
Yes, we can do a lot that we couldn't do 30 years ago. However, this _only_
falls under the banner of "AI" because AI has been redefined from the promises
of 50 years ago [1], when the goal of AI was to build "human-level"
intelligent agents. The "Dartmouth Summer Research Project on Artificial
Intelligence" proposal [1] in 1955 when revered AI researchers Marvin Minsky,
(inventor of Lisp) John McCarthy, and (inventor of information theory) Claude
Shannon claimed "a significant advance can be made in one or more of these
problems if a carefully selected group of scientists work on it together for a
summer". They failed at this lofty goal of solving the AI problem over a
summer, and over time the field of AI was _redefined_ to be narrow fancy
mathematical tricks to solve domain-specific problems. Most "AI" is not
intelligent at all, instead they are approaches for searching some search
space with a non-general-purpose tool, such as genetic algorithms, neural
networks, or first-order logic theorem provers. A "true AI" would discover
regularities [2], or patterns, in any search space, and exploit these patterns
to incrementally improve its ability to navigate the search space. Examples of
regularities in the world are that navigation is often best performed by
sticking to the regularly appearing feature that we call a "road" instead of
attempting to climb over buildings, and that navigation can be effectively
guided by attempting to minimize the Euclidean distance between the current
location and the goal location. Instead, most of the currently available tools
are essentially performing a stochastic search through the state space,
possibly guided by some heuristics.

While a lot of progress is being made in the redefined and inaccurately named
field of AI, object recognition and speech recognition are still narrow
domains. They are not using general purpose approaches, but instead tweaking
and tuning specific algorithms to these areas. In reality, I am not
championing for "human-level AI", as I think it would be very difficult to
duplicate a human's strengths and deficiencies [3] accurately. Instead, I am
saying that we should be encouraging research that produces a general
intelligence; software that can recognize and exploit regularities (patterns)
in the state spaces of many problem domains, and apply the abstracted
knowledge gained from earlier experiences to new domains and problems. Such
software would have a tremendous impact on society, assisting us in solving
and analyzing many problems and likely leading to an "intelligence explosion"
after crossing some threshold at which the software is able to recursively
improve itself. [4]

You asked "just about every specific useful task humans do better than
computers, there are a few dozen AI researchers working on it. Why is that not
a reasonable approach to the problem?" How do we take all these domain
specific solutions and get a cohesive general intelligence out of them? I see
these domain specific algorithms as being possibly useful to a general
intelligence as an _optimization_ to shift some of the load off of the more
general purpose algorithms, but I don't see how throwing a bunch of face
recognition, path finding, neural network and genetic algorithms into a box
would automatically cause a general intelligence to pop out. These research
areas seem mostly tangential or supportive to genuine Artificial General
Intelligence research.

1\. <http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html>

2\. <http://en.wikipedia.org/wiki/Kolmogorov_complexity>

3\. <http://www.singinst.org/Biases.pdf>

4\. <http://en.wikipedia.org/wiki/Technological_singularity>

~~~
machine
I think the problem is still that there isn't really a clear definition of
general intelligence or how it should be embodied on a computer. I agree that
putting a bunch of different algorithms for different tasks in a box won't
create something people would be willing to call general intelligence, but
what do you expect a general purpose intelligence algorithm to do exactly?
That is, if I handed you one, how would you test it? What are the inputs and
outputs?

Don't get me wrong, I'd love to create a general artificial intelligence. It's
just not clear to me what that means. In the absence of a good definition of
the general problem, I think it's perfectly reasonable to pick an extremely
hard specific problem (like visual object recognition), and focus on that,
under the assumption that in order to completely solve this specific problem
you will end up solving the (undefined) general problem.

"A "true AI" would discover regularities [2], or patterns, in any search
space, and exploit these patterns to incrementally improve its ability to
navigate the search space." -- Actually this sounds like Eurisko, a heuristic
search program that modifies it's own heuristics
<http://en.wikipedia.org/wiki/Eurisko>

Also, it's worth pointing out that the different techniques used in different
subfields of AI are not always that different. There has been some work in
creating very general powerful frameworks that explain a lot of specific
algorithms, like Markov logic networks
<http://en.wikipedia.org/wiki/Markov_logic_network> which are sufficiently
powerful that they subsume all of logic and statistical graphical models
(which is to say maybe 90% of all recent machine learning algorithms). With
these sorts of general frameworks new algorithms and approaches in one
subfield get ported over to other subfields, and there is more interaction
then you might think.

~~~
jey
Shane Legg and Marcus Hutter of IDSIA did some recent work on a metric for
machine intelligence. ( <http://www.vetta.org/documents/ui_benelearn.pdf> )
Marcus Hutter's PhD thesis produced a theory of "Universal Artificial
Intelligence" called "AIXI" based on Solomonoff induction. Sadly, AIXI is
incomputable, and the time and space bounded version "AIXItl" is still
impractical. <http://www.hutter1.net/ai/aixigentle.htm>

But you're right: we don't know much about intelligence nor how it should be
embodied on a computer. This is why we should encourage some really smart
people to focus on the problem! I sincerely think this is an area that would
yield to research. Might take a long time and a lot of brainpower though...

Lenat's Eurisko is pretty cool, and I don't know much about it, but it doesn't
sound like Eurisko is as general as is needed.

------
ced
Sounds like a funding proposal. He makes a lot of strong claims regarding his
own abilities.

Fun quote from McCarthy: "My own opinion is that the computers of 30 years ago
were fast enough [for AI] if only we knew how to program them."

------
mynameishere
Re: "AI Manhattan Project"

The Manhattan project involved almost no research (1). It was an engineering
project whose constraints were manpower and money. The research was a somewhat
more low-budget affair, viz, szilard daydreaming around London, Fermi working
in a 3rd rate Italian lab.

(1) One of the few actual innovations was the explosive lens.
<http://en.wikipedia.org/wiki/Explosive_lens>

------
jimbokun
Skimming the article, I don't see how it's feasible to ethically engineer a
"human equivalent intelligence".

The author talks about engineering a toddler level intelligence, the reasoning
being that the toddler program would not be capable of figuring out a way to
"escape" its box and getting into mischief that humans would be unable to
control or prevent.

But the thought of "turning off" a toddler equivalent makes me queasy. Will
the toddler think it is a person? What an awful, worse-than-Truman's-World-
nightmare it would be for that "toddler" to find out the truth. Could we
ethically "turn off" such a program?

Even if it knows that it is not human, if it is truly equivalent to a toddler
it is almost certain to introduce some kind of ethical quandries we cannot
possibly predict before the program actually exists. I do not see any safe way
to pursue this project and successfully avoid ethical landmines.

I am also troubled by the vaguely utopian hopes this man has for his personal
project. Attempts by people to implement any kind of utopian vision tend to
end badly.

