
Creating a marketplace for artificial intelligence - un
The applications being created on the web today aren't as sophisticated as they could be, the reason being that to create something large enough to offer as a complete product requires tremendous resources. For example speech recognition is produced by only by two giant companies (Microsoft and IBM), and some level of text analysis in search engines (google, yahoo, microsoft). Computer vision is being actively pursued at large companies and yet they still cannot release a product yet.<p>An  alternative way would be for small companies/individuals to start producing small fragments of functionality and offering it in a per for use basis. (as in a cat recognizer in an image, or a recognizer for the word "hello" in an audiofile).<p>To create the above recognizer would require gathering data (such as hundreds/thousands of pictures of cats) and feeding it to a supervised learning algorithm like svm, boosting etc.<p>Thousands (perhaps millions) of people creating their own "classifiers" and charging for them (perhaps 1000 uses for 1 dollar) over the web might lead rapidly to seeing/thinking machines.<p>(A similar model can also be used to pose competition to entrenched monopoly software like mathematica/maya/autocad - thousands of people creating and charging for small bits of functionality)
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bengoertzel
A bunch of classifiers is not an artificial general intelligence. A human-
level mind (or beyond) requires a systematic cognitive architecture, it's not
going to emerge out of a heterogenous, quasi-random soup of mind-components.
This is a naive theory of cognitive science, I would argue.

For a careful, systematic design for an advanced AI system, see
<http://opencog.org/wiki/OpenCogPrime>, which is associated with the open-
source AI project opencog.org

Anyway, I'm probably one of the biggest optimists in the AI research
community, but in my view the idea in this post represents "way over the top"
optimism based on a naive view of how mind works.

Given a mature version of an AI system built according to an overall AI
architecture like OpenCog, then maybe people writing little "mind modules" as
you suggest could make sense. But the Internet just is not a "mind operating
system."

I wrote about what would need to be done to turn the net into more of a mind-
OS in my 2001 book "Creating Internet Intelligence" (Plenum Press). At the
high level I don't think this is a bad idea. But again, brains are not just
soups of heterogenous processes -- the right high-level cognitive architecture
is required.

\-- Ben Goertzel novamente.net agiri.org singinst.org goertzel.org

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un
I'm trying to draw a parallel between how content on the web is being created
and how an artificial mind might be created. If you sat down at tried to
engineer a giant encyclopedia within one company/community, you would end up
with britannica (within one company), and wikipedia (within a community of
enthusiasts), However, the web at large is much larger than both of these
because of the economic incentives for people to create articles. Many people
try and fail (in effect working for free), but the progress is rapid, as in
evolution. In the same way, incentivizing people to produce classifiers would
have the similar effect of rapid progress.

If you think of intelligence as being the ability to predict accurately,
having a giant web of classifiers that predict accurately could be construed
as a form of artificial general intelligence, or a human type artificial
intelligence. Having an enough data about the world, because you have created
many classifiers that can recognize the semantic events in video and speech,
would allow you to make all the same types of recognitions and predictions
that a human would make.

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lacker
I disagree that creating a useful artificial intelligence product requires
tremendous resources. When search engines first came into existence, a few
machines for a crawl and a few machines to do basic posting-list serving were
enough to have a useful product. Now it's tougher but Cuil with a dozen
engineers has done a reasonable job from scratch.

The real problem is to find an application for artificial-intelligence-style
programming that is actually useful. What good is classifying what's in a
picture? If you could sketch out a useful product then the right small group
of engineers could build it.

~~~
un
Surveillance technology is one such application already in use (location of
faces and people in images), video analytics on google. A much larger market
is robotics. (Factories use robots that are custom programmed with vision for
each application and don't really do recongition in the sense of object
recognition in an arbitray scene). Recognizing common household objects would
be useful for a company building home robots (of which there are a few) right
now.

There is a recent startup doing image recognition using humans, on photos at
flickr.

Search engines relied on letting a single algorithm go out and process data.
There isn't yet an artificial intelligence algorithm that can just go out and
do what it needs (unsupervised learning algorithm that works well). The
currently available algorithms (supervised learning) require labelled data,
and since there are millions of categories of things (that we as humans can
percieve and so are useful to recognize), it would require at least a billion
labellings of the data (1000 data labellings for each category).

The trouble with companies and groups engineers is that you would have to fund
them, organize etc. and only a few groups would form (there are only an
handful of video analytics companies probably, all of them probably do
recognition of a handful of the same things). Besides, the system advocated
above is pretty simple, hardly any engineering in the sense of specialized
technical skill is required. You would just collect and feed data into the
classifiers. Others would combine data from classifers into yet more
classifiers.

~~~
un
Another application is in mobile phones that recognize objects, already being
done in japan for certain objects in stores. There was also an application
developed for the android platform that did some type of recognization of
outdoor environments.

~~~
lacker
Sounds pretty cool. So why don't you make a mobile phone app that recognizes
some sort of useful object to recognize? That doesn't sound like it's too big
of a project for a startup.

Even if a large company was to throw a huge amount of resources at this, they
would still probably have a team of a few people make a demo first. They go
through the same process as a startup in a sense, first having a few people
trial and demo and if it seems good then give them more money and/or
engineers.

~~~
un
My point is about the "democratization" of classifer creation. I personally am
not a programmer and don't have the talent to create a startup, and there are
millions more people like me. However, if there were a place where i could
feed some data and have a classifier hosted somewhere I could participate in
this.

It's the same way blogs allowed many more people to start creating content on
the web, whereas previously it required creating and hosting a website. Or how
google app engine, might increase the creeation of web applications because
previously a lot of people didn't have knowledge of administering and scaling
unix/dbms based applications.

A large company would seriously have a look at roi, whereas many small
entrepreneurs just have a go at it. Britannica (large company) is smaller than
wikipedia (community of individuals), which is again smaller than the content
on the web at large (community of incentivized small entrepreneurs).

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showerst
I think you're defining artificial intelligence too narrowly.

If you branch beyond the classic uses (speech, vision, text-classification) to
some of the things that have been more recently branded 'Collective
Intelligence', there's really a large (and quickly growing) field out there.

Bayesian filtering is standard in anti-spam solutions, recommender systems and
price modelers are huge in online shopping and rental verticals, and social
networks are starting to use things like clustering to connect people.

AI is making some huge advances (especially in the past 2-3 years as the 'open
data' culture gains steam and the tech catches up) and I think there's a lot
of room for startups in the space serving niche purposes like custom
recommenders, especially when you throw data-visualization into the mix.

Examples off the top of my head include Farecast, the airline backend at ITA
Software, and some of the upcoming work in the automated rss recommender area.

~~~
un
True, they are applications of machine learning techniques and can be
considered "narrow ai". What I was suggesting is more along the lines of
something that can do the tasks of children, see and recongize most things,
and understand language. I think one way of achieving that is by
"bootstrapping" from understanding of video, If an understanding of what a
human does in the video (standing, sitting, limb positions, height, facial
expression) in relation to objects and other humans can be automatically
inferred, a predictive model could be built with enough data of what a person
in any given situation might do. Combine this with robust speech recognition
and understanding of the behaviour of common objects I think you would be
closer to cracking the goal of language understanding (which I think defines
true human level intelligence).

~~~
showerst
And i'd go so far as to say that this wouldn't just be good business, it would
revolutionize human life.

But from what little I understand of the history of AI, people have been
throwing resources at this category of problem for 40+ years and made only
middling progress.

Not that that means that people (especially saavy entrepeneurs) shouldn't
tackle the problem, I just think that by taking the constrained view (and
shooting for the moon) you'd be missing a ton of low-hanging fruit in other
areas.

With the emergence of commercially viable computing on demand we're tearing
down a major barrier to AI, but I think that throwing the idea out and saying
'Hey! Why don't more of us get together and tackle this problem?' betrays a
bit of an underestimation of just how tough this problem is.

That said, I would love for you to prove me wrong =) And as another poster
mentioned, groups in the areas of surveillance and security (where the money
is, at least initially) are making good headway on the problem.

The biggest problem with building predictive models, as another poster pointed
out, is feeding them tagged training data. You could really feasibly load up a
database with terabytes of data for most of the stimuli people react to these
days (or will be able to in the near future), and even most of the basic
combinations. It's a matter of tagging and classifying them in the first place
that's the problem. Maybe with enough Indians sitting in front of mechanical
turk... _scrambles off to a calculator_

~~~
un
That's exactly my initial reason for posting this. In the singulariy
enthusiast community artificial intelligence is the most important thing we as
humans can create (post scarcity, immortality etc).

What I was suggesting is that a "gold rush" type situation form around the
building of classifiers, such as that occurred with website creation. There
are millions trying to get an income onlne creating content for adsense and
affilate programs (and for free with wikipedia and open source). Most will
fail at generating reasonable income but in the process a large amount of
wealth has been created. The same could be done for classifiers/artifical
intelligence. It would require just collecting a thousand or two pieces of
data (image/sudio/text) to create a classifier, an effort similar to creating
a small website.

The problem is that even in the past there really wasn't a large number of
people working on artificial intelligence, maybe just a few hundred or
thousand, and computing power wasn't cheap enough to make it work. Computer
scientists that do work on it are trying to create more unique algorithms that
can be published, rather than creating applications with existing working
algorithms (after all they''re paid to create new things, not simply apply
what's available). In Larry Page's lecture at AAAS he mentioned that there are
less than a hundred people working on artificial (general) intelligence today.
(you can see it on youtube).

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jonmc12
What platform could support this vision? Also, how will you get all these
disparate classifiers to integrate and scale without getting bogged down in
complexity?

btw, on the computer vision example, I believe this is what Numenta is doing
to an extent.

~~~
un
You can use open source libraries such as opencv/torch/torchvision and
libraries in R for the machine learning. Haar recognizer is one such algorithm
in opencv/torchvision that can locate faces in images.

As for the complexity, that's what the market would sort out. People would
build on other's work, and everyone would get compensated for as much as their
work is used.

Numenta is an example of the monolithic comapny doing all the work, and that's
why you can't use or build on any of their technology yet (incidentally, they
use bayesian type algorithms while i'm advocating easy to use standard
algorithms that have many open source implementations - boosting, random
forests, support vector machines, and maybe neural networks).

A startup opportunity would be for a company to host the learning algorithms,
and just have people pay for and submit data from which classifiers (academic
jargon for recongizers) can be built and returned. (The company would have to
be trusted though as they could simply keep a copy of the created classifier
and resell it).

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hooande
Great idea. Start working on a prototype and make sure you apply in time for
the winter YC session.

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bootload
_"... speech recognition is produced by only by two giant companies (Microsoft
and IBM), ..."_

and ATT ~ <http://www.research.att.com/~ttsweb/tts/index.php#top>

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vaksel
Sure it could be done, but who is going to pay for it? At $1/1000 uses you
won't even be able to cover your hosting fees.

~~~
un
At 30 frames per second you would start to earn dollars in just a few minutes
of video. The price of 1 dollar per thousand would actually be too high for
most applications - there might be thousands of object recognizers applied to
each image.

~~~
un
A recongition task would take at most a second, to earn 100 dollars would
require a little over a day of computing at that rate.

