
OpenCyc 4.0 is out - wslh
http://blog.markwatson.com/2012/07/nice-opencyc-version-40-has-been.html
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DavidPlumpton
Looking into Cyc I infer the following...

Q: So what is Cyc used for? A: You could use it for games Q: Has it been used
for a game? A: No

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creamyhorror
I looked at OpenCyc previously while investigating semantic networks, but not
long enough to figure out how to do things with it. I'd be interested in
hearing about what any of you have used it for. A map of English knowledge and
relations between concepts ought to be pretty useful for some things.

~~~
cdcarter
I just tried to see if there was something I could do easily with it that
would be cool to learn about. Still have not successfully started up the
engine without closing all other processes to free up memory.

~~~
mark_l_watson
I was running it on a 4GB MacBook Air and I shut down other memory hogging
programs like IntelliJ - then everything worked fine. I recently rented a 32
GB server for running a large RDF data store, and that is where I am trying to
relearn OpenCyc (I spent a lot of time with version 1, but became interested
in other things - I am going to give version 4 another chance).

I wrote that blog article. I would appreciate any feedback on the utility of
loading the RDF data into an OWL supporting RDF data store (I have an
Enterprise Stardog license, which is what I would use) or use OpenCyc as a
service.

Years ago, the OpenCyc usenet groups were useful so I need to check there.

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gojomo
Is there any public demo using OpenCyc that does something resembling
anything?

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ratsbane
Link to the primary source: <http://www.opencyc.org/>

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PaulHoule
my hunch is that Cyc still has about 20% coverage for the probability density
of meaning in text... just not quite enough.

Just increasing the size by 5x won't do because it's a problem like modelling
human hair, at some point the snags, knots and frizz will just drive you
insane.

~~~
mark_l_watson
That was my blog article. We are getting better tools for knowledge management
such as RDFS/OWL, lots of linked data sources, perhaps (Open)Cyc, and
companies like Google demonstrating great things that can be done with
statistical NLP to solve tough problems like machine translation.

All that said, I think we have a long ways to go before we can build 'real' AI
real world knowledge based systems that understand natural language text.

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bane
I remember reading about Cyc when I was a kid, thinking it was pretty cool. I
remember reading breathless articles with emotional quotes from the system
where it asked existential questions like "am I alive?". I grew up with Sci-
Fi, Video Games and Anime where the protagonist had an AI that did all sort of
wonderful things for them. I'm by default a _fan_ of the Cyc project and cheer
a little whenever I see anything about Cyc.

So this is not meant to take anything away from the folks who work on
(Open)Cyc, but I think it's time to ask some hard questions about this kind of
AI approach:

Cyc (open or otherwise) is about 30 years old and has yet to demonstrate any
sort of serious advancement over other, much lower friction approaches
(actually it's hard to find utility at all). My personal feeling is that it
represents a failed approach. This is not the same as a failure, it was a very
important experiment that tried to answer the question (paraphrasing the WP
article), "is it possible to craft an AI that can perform human-like reasoning
using an ontology of common sense knowledge?" I think after 30 years the
result has been an interesting curiosity, but the answer is a pretty strong
"no".

There are more important questions though. Let's say that OpenCyc represents
30% of the state goal -- and since the goal from the WP page is a bit nebulous
let's refine it and say 30% to the reasoning capacity of a 6-8 year old human
child (6-8YOHC). And let's say that miracle upon miracles, Cyc is able to move
closer to that goal at a steady 1% per year...and after 70 more years Cyc is
now as smart as a 6-8YOHC. Is this desirable? I mean, is this a good thing to
have? I don't mean this question to come from a source of deep philosophy or
moralistic questions...but have you ever tried to get a 6-8YOHC to do any task
more complex then follow a few simple instructions with lots of supervision
(even getting personality and desires of the child out of the way)? A digital
6-8YOHC measured just on intelligence _might_ , just _might_ , be smart enough
to follow a simple question like "find me news articles about the Libor
scandal". But is that worthwhile? Is 100 years of R&D to achieve this useful?

The approach in the past was to not tackle the general purpose problems the
Cyc was always meant to but to focus the approach on specific problem sets and
build "expert systems". This is apparently what the (limited) list of use-
cases Cyc is being applied towards is. This again means that Cyc has failed.
"Common sense" knowledge means "general purpose" a 6-8YOHC knows as much about
terrorism or biomedicine as it does about car engines...in other words it
should perform equally well on any of those things because none of those
things are "common sense". "It's hot outside so I shouldn't wear a winter
coat" is a common sense thing.

So what are we left with? We're back to ontologies and various flavors of
knowledge management and reasoning systems, all of which in my experience are
faddish cover terms for the kind of AI research which just didn't go anywhere
and haven't demonstrated much more than adding an interesting aside to what
were already pretty darn good search results in my favorite search engine when
I go to hunt down showtimes for a movie I want to see.

The number of dead, dying and useless Semantic Graph projects that are out in
the world are astonishing. The lack of any real useful tools built with them
is even more astonishing. And I don't mean tools that let you inspect the
Knowledge Base (very exciting stuff like e.g. "is President Obama in the
Knowledge Base? What is he connected to?") which I guess are useful sort of
search engines in some contexts where the information is impossibly well
curated (e.g. wikipedia). But I mean things like automated teachers, digital
accurate diagnosticians, AIs that write good Pop Songs and hold interviews
with the entertainment press, _anything_ that _might_ represent intelligence.
It seems like whenever some of the pieces of these things show up (WP, Khan
Academy, etc.) they are eschewing the direction that Semantic Research is
taking us and going with a lower friction, higher payoff route. Why isn't
there an AI that can suck in WP and Project Gutenberg and then generate and
give me Khan academy lessons for K-12? Or at the very least provide NPCs in a
game that are capable of having at least a 6-8YOHC level of conversation.

The answer has always been "wait till we add more
objects/entities/facts/assertions/things/stuff/etc.". This is a bad answer,
and unfortunately is the one that challengers to this approach get served back
to them. Feeding "stuff" into a model and waiting for the spark of life to
kick in just doesn't seem to be cutting it. There are ontologies with
literally _billions_ of things in them that are nothing more than strangely
structured databases with odd query interfaces.

I'm a fan of Cyc, and it feels like it _makes sense_ , and I'd love to be
proven wrong, but I'm forced to lump it and similar approaches into yet
another dustbin of evolutionary dead ends along with atomic powered cars and
dinosaurs. I'm afraid Cory Doctorow was right
<http://www.well.com/~doctorow/metacrap.htm>

I'd love to know for example that Cyc, as the most mature common sense
reasoning system was the "go to" when Google started building the voice search
in Jelly Bean and the Google Semantic Search and every few months we'll just
be blown away by what's happening when we turn over a 30% 6-8YOHC on nearly
every bit of human knowledge ever collected and it'll start doing really
really useful amazing stuff for us instead of saving me a few smudges on my
screen when I want to set an appointment or look up how tall Brad Pitt is.

I hate to turn this into a vent, but I just feel so... _crestfallen_...at the
seemingly limitless lack of serious progress in the entire field (not just
Cyc) despite what are probably millions of man years put into it.

The final hard question then is...is this kind of approach the right one?

~~~
mark_l_watson
+1 a very good vent, and anyone who has had a long term involvement with AI
can agree with parts of or most of.

Do you really think that we should give up after 50 years of little success in
the really difficult AI problems? I think we should keep trying. Failures are
OK if they are affordable and we can learn from them.

~~~
bane
To be honest, I don't know. Six months ago I was ready to write off the whole
endeavor, then I see things like this
[https://plus.google.com/100130762972482716067/posts/BN5qjTEN...](https://plus.google.com/100130762972482716067/posts/BN5qjTEN62r)

and feel like mankind _just_ took a little baby step in the right direction.

I don't think a 6-8 year old human child (6-8YOHC)is the right goal. Or a Star
Trek like computer is either, talking to computers gets boring (talking to 6-8
year olds gets boring too!). It's the AI equivalent of the Gorilla Arm in
interface design (perhaps "Gorilla Mind" is a good term?) and like I said, is
achieving this really _useful_? Even if achieving the near term goal isn't
useful, does it lead into a next goal that's useful?

I think the right goal is to brain storm ideas, "what would we like a computer
to do for me?" then start inching towards those.

As an off-the-cuff example, build an AI scaffold of some sort, point it at
Wikipedia and Project Gutenberg and have it generate a Khan Academy style
educational program for K-12. Get it to start teaching, then use the feedback
from the teaching to better model how humans think and learn.

There, that's my contribution to the world of AI. A quick brainstorm with
something that could be useful for people. I would find it hard to believe
that after half a century, researchers in the field haven't come up with
similar kinds of exercises. But I keep seeing more myopic answers.

Getting an AI to tell me a famous movie star's shoe size is not interesting
because it really only saves me time, but doesn't do something new for me.
Likewise expert systems for diagnostics, any experienced human with a
reference can do this job! AI seems too focused on replacing the "human who
can do this job with a reference" (RAHWCDTJWAR) and not enough on augmenting
what humans can already do to make them better, or doing complex tasks like
instructing a class. The problem is not that we're emotionally driven to keep
the "human with a reference" in the equation, but that over time, that human
has proven to provide better results!

Any brainstorming ideas that fit this mold _should_ be rejected as vectors for
the field. If it even smells like a project is turning into a RAHWCDTJWAR run!

Similarly the 6-8YOHC is the wrong direction. Let's be honest, 6-8YOHC aren't
very useful or knowledgeable. Let's stop trying to make AIs that are do-
nothing ignoramuses. I don't need an AI that knows that when it's hot out I
shouldn't wear a winter coat. I _already_ know this.

Like I said at the beginning, I think Cyc and similar approaches have been
_valuable_ as a line of research and inquiry, but have ultimately provided so
much failure and so little progress, it's obvious that it's not the right way
to go. Knowing this is so very important. But I keep feeling like this message
isn't getting across and this basic approach to AI has long _long_ overstayed
its welcome.

~~~
waterlesscloud
I understand where you're coming from. In my college days I bought all those
thick academic books on "machine learning" when the term meant Lenat more than
it meant statistics.

That route does seem to have failed us. Or at least not have gone much of
anywhere in the years since. At the same time, I'm not sure the Google-y
approach to machine learning has made real progress in the last 10 years.

Sure, we've got cars that may or may not be able to handle actual road
conditions, but search, and more importantly any sign whatsoever of computers
knowing what we want to do has stalled out for quite some time now.

I dunno. There's got to be another approach that yields more progress than
either of the semantic or statistical paths.

~~~
bane
_That route does seem to have failed us. Or at least not have gone much of
anywhere in the years since._

I'd like to reference the recent Higgs result as a compare contrast example
from a different field.

The search for the Higgs is slightly younger than the search for AI - but of
about the same age so it's worth comparing. It took a _very_ long time to
yield basic results -- namely "does it exist?" The search for the Higgs was
pure Research. The day after the Higgs discovery nothing changed in the world
except that we now know it exists. Given 20-30 more years of R&D we _might_
get a hoverboard, or faster blenders or something, and the total time
investment will have been about 70 years from "notion on a chalk board" to
"hoverboard".

AI researchers might use the Higgs as an example to not poo poo their field
since they are still in that long _long_ time between theoretical proposal and
working discovery. Detractors might say "but all you propose is just shoving
more factoids into your AI model hoping it springs to life"! The analogy with
the Higgs is that researchers were for a long time simply proposing to build
bigger and bigger accelerators until the Higgs fell out.

I'm not a physicist but I'm hoping that there was a stronger theoretical
framework surrounding the Higgs then "let's keep crashing stuff into each
other harder and harder till what we want comes out of it". Likewise, I'm not
a Cyc-style semantic AI researcher (or an AI researcher of any particular
type), but I'm hoping that the field has more going for it than "let's keeping
tossing factoids into our Semantic Graph until it springs to life".

I'm willing to think that a 70 year R&D time is worth it if we end up with
commercially ready 1.0 equivalent Minds at the end
<http://en.wikipedia.org/wiki/Mind_(The_Culture)>.

But at this point I don't think we're any closer to this then we were 20 or 30
years ago. It's a perpetual Research horizon at this point. To put it back
into perspective with the Higgs, 15 years ago they were starting construction
on the LHC.

AI researchers will lament the lack of funding in their field, etc. But I have
yet to hear a compelling research direction the field would go if it were
suddenly gifted the cost of an LHC or two to advance the field.

