
Computer Vision is Artificial Intelligence - fogus
http://quantombone.blogspot.com/2011/03/computer-vision-is-artificial.html
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GrantS
I grew up reading Asimov's robot stories and knew I wanted to work on
artificial intelligence growing up. I realized when I got to college that
computer vision was the closest thing to what I had always _thought_ AI
research would be like. (So I did a PhD in computer vision.)

On a related note, David Lowe, of SIFT fame, once related to me a story about
when Isaac Asimov visited his research lab in the early 1980s and when they
explained to him that computer vision was about enabling robots to see the
world, Asimov shrugged and said he never realized that was even a problem --
he just assumed robots would be able to see.

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jgershen
If anyone wants to work on "artificial intelligence," then, GazeHawk is
hiring!

(sorry for the spammy post, but I'm really impressed by the level of computer
vision experience and talent showing up in this thread, and wanted to reach
out to you all. We are a startup which is also actually solving hard CV
problems)

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smogzer
I quit my Phd in computer vision. There where i was working with a stereo
vision robot. So i was trying to get the robot to behave like a human would,
fixating point of interest (salient points) and calculating distances, well
that involved a lot of trigonometry and the cameras had to be perfectly
calibrated to get good data, so it was really hard just to match points of
interest from both cameras. In conclusion i believe robots will see the world,
but it will be through their eyes/sensors, not some human like approach. As
for AI it will have to be some sort of baby like bootstrapping aproach able to
learn, validate, propose new theorems and have it's own curiosity, in the form
of a function that minimizes entropy or something like that.

And it will be funny if humans won't be able to make sense of that learning
data :)

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pixcavator
>>i believe robots will see the world, but it will be through their
eyes/sensors, not some human like approach.

One of the biggest, and very immediate, obstacles in computer vision is the
inability to extract 3d information from 2d images. My feeling is that by the
time this problem is fully solved 3d images will become ubiquitous.

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jimwise
It's certainly no less AI than natural language processing is -- both seek to
use algorithmic approaches to resolve the ambiguities in a bunch of input
captured from fuzzy real-world sources, and build data structures representing
that data -- as best understood by the program -- that can be manipulated by
more traditional processing algorithms.

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quantombone
I agree that NLP is no less AI than vision. What I wanted to point out in my
post (I am the author) is that too many smart computer vision researchers
don't view vision as AI at all. Somehow many people's research program is
centered around the assumption/desire that a pure image classification
approach will solve vision.

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alantrrs
Computer Vision is in fact (according to Shapiro[1]) AI-complete, meaning that
the solving this problems is equivalent to solving the central artificial
intelligence problem. It is in my opinion one of the hardest problems to solve
that is why Researchers have to focus on a tiny specific topic in computer
vision. 1\. <http://www.cse.buffalo.edu/~shapiro/Papers/ai.pdf>

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giardini
"... solving this problems[sic] is equivalent to solving the central
artificial intelligence problem"

And that would be, precisely, what?

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alantrrs
Making a machine "think" the way humans do.

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T_S_
I have a degree in Machine Learning. Recently I started working on a
smartphone app involving CV. I am working closely with CV researcher.

And guess what? They use vectors, linear algebra, probability and classifiers
too! They just go to different conferences to talk about them. If you learn
enough math you can see through most of the terminology differences.

Those of us in the so-called real world must never forget the harsh industry
structure of academia rewards fragmentation and obscurity, not unification and
accessibility. It's the fault of the customers (grant makers) for rewarding
this labor maximization behavior.

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mhewett
Deep Blue was "in the early days of AI"??? The OP needs to study the field
before making such pronouncements. There were many breakthroughs in AI 30
years before Deep Blue. See, for example, Arthur Samuel's checker program from
the early 1960s. <http://en.wikipedia.org/wiki/Arthur_Samuel>

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vmind
My AI lecturer remarked that AI was a great term for hard problems, and that
once a field or area was effectively solved or developed, it no longer got
called AI.

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cryptoz
That's the AI Effect: <http://en.wikipedia.org/wiki/AI_effect>

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Devilboy
"AI is always defined as anything a computer can not yet do"

\-- forgotten

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endtime
"Artificial Intelligence" is both an umbrella term for a number of CS/stats
approaches to certain types of problems (graph search, classification, etc.),
and also a term often used to mean the same thing as Artificial General
Intelligence (i.e. an artificial mind).

Vision is trivially covered by the former definition, and clearly not by the
latter.

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akerfonta
I would say that while there are indeed a number of approaches to various
problems, viewing AI as a series of only vaguely connected problems is a
mistake. A true AI needs a new way of looking at things as they all relate to
the system as a whole.

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endtime
Did you actually read my whole comment, or just the first few words?

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drpgq
As a researcher working in computer vision, personally I prefer not having
computer vision associated too much with AI just because of the pejorative
aspects that the term artificial intelligence has had in the past.

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joe_the_user
Nice little, makes me think...

 _"I believe that once we have made progress on vision (not in the narrow-
universe setting) to the point where generic visual scene understanding is
effectively solved, there won't be much left that needs to go into the
"ethereal" mind which cognitive scientists want to empower machines with..."_

This is a great point but it may have implications that won't make the author
happy.

If the field of vision computer vision is "nearly as big as" the field of
general artificial intelligence, it may not be a good direction from which to
approach general artificial intelligence.

The vision problem involves low-level problems of light-physics-etc, mid-level
problems of object transformation and distortion _and_ high level problems of
cognition. Saying that vision is hard is saying we need to find one algorithm
which can fluently mix these levels rather than using several algorithms that
just sequentially deal with each level. And such a level-mixing algorithm sure
sounds close to general IA.

It fits with argument of people like Jeff Hawking that there's a "Kernel" of
general AI that computer vision is just an instance of (see
[http://books.google.com/books?id=Qg2dmntfxmQC&lpg=PA101&...](http://books.google.com/books?id=Qg2dmntfxmQC&lpg=PA101&ots=6ixoIbLmg_&dq=%22the%20neocortical%20algorithm%22&pg=PA101#v=onepage&q=%22the%20neocortical%20algorithm%22&f=false)).
But there's nothing there that says computer vision is useful direction to
approach that Kernel from - the technical considerations of vision are hard
and you haven't shown it provides a particular useful division in a divide and
conquer strategy.

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quantombone
While vision might not be the best avenue for studying general purpose
intelligence, it is worthwhile to think of vision as more related to hard AI
and less of a problem of applied machine learning.

I think once vision researchers understand just how close they are to hardcore
AI, they will realize that there is a plethora of knowledge (not all visual)
about the world that can be used to create better object recognition systems.

-Tomasz (blog post author)

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barista
You can experience this first hand if you have ever played or hacked with
Kinect.

