Ambitious Artificial Intelligence Project Operating In Near-Secrecy For 30 Years

... and then "went dark", "officially" working on this tedious, brittle attempt to compile common sense into a graph, which yielded nothing in practical application.

It's like we're in a movie and we're about to get a big reveal that he's really been using Eurisko to solve major untouchable problems.

Under the hood it was basically genetic algorithms over a meta-object protocol to extract well-scoring arrangements from ontologies, which is damn interesting, but combinatoric complexity bites you every time.

Kenneth Haase published a couple of papers dealing with some of the issues in more depth.

[1] http://eksl.isi.edu/files/library/Lenat_Brown-1984-why-AM-an...

Essentially, we would take something like:

Gov. Rick Perry has said he will no longer wear cowboy boots, which some believe is part of an attempt to soften his gunslinging image as he considers another run for president.

And map it to something more machine-readable:

  "Gov." -> http://dbpedia.org/page/State_(polity) (10% confidence)[1]
  "Rick Perry" -> http://dbpedia.org/page/Rick_Perry (95% confidence)
  "Rick Perry" -> http://sw.opencyc.org/concept/Mx4rM7N6iOeUSpGar4HaqXF3zg (95% confidence)
  "Cowboy boots" -> http://dbpedia.org/page/Cowboy_boot (75% confidence)[2]
  "President" ->http://dbpedia.org/page/President (90% confidence)[3]

DBpedia: Tons of machine-readable information like Party, Alma Mater, birthday, spouse, etc. Extensive categorical links (dcterms:subject) like category:United_States_presidential_candidates,_2012.

OpenCyc: Knows he's a politician affiliated with "Republican Party," "Democratic Party," "The Republican Party," and "The Democratic Party"

I highlight only one example, but this is all over the place. Opencyc has duplicate terms where dbpedia/wikipedia does not. Opencyc has far less information. Opencyc has more incorrect information.

This is inevitable when you consider the two approaches. Wikipedia has tens of thousands of people making connections and updating the resource, where opencyc relies more on scripts. Opencyc, and quite possibly Cyc, is already antiquated by Wikipedia.

[1] Note that the confidence was estimated from heuristics I wrote based on how the ontologies were put together

[2] OpenCyc typically does not have mappings for plurals, while wikipedia has a very convenient redirect system for string mappings

[3] OpenCyc matches President with 60 concepts. Too much noise to do anything with

And here is how that page looked in 2003: http://web.archive.org/web/20031204204518/http://www.cyc.com...

Quite a few of the publications are missing from most recent one. It is also worth noting that news page link was broken from 2003 until recently.

Does not seem to be a very good way to go about revolutionizing any technology...

As any entrepreneur will tell you, it is never a good idea to have long-running projects with few public deliverables. Projects like this need new blood, new ideas and continuous user validation in order to remain relevant.

Is it? How do we know? You've been working on a product "in stealth" for 30 years, shown practically nobody outside your company, and provided us with nothing more than a vaporous description of its capabilities (in Businessinsider, no less). I'm left wondering not only how seriously I should be taking this claim, but also exactly what the claim even is.

Artificial intelligence is hard. I did an internship at Numenta [1], where Jeff Hawkins is approaching AI from this same biological-first model. He hypothesized how a small subset of the brain works, constructed a model of it, and admirably hired dozens of engineers to build it. The guys behind the Numenta software are some of the best engineers I've met, with combined centuries of experience, and it's taken them almost ten years to get the software to its current, extremely primitive state. Right now, the model is implemented, and you can use the API to apply it to specific applications (predictive analytics, anomaly detection). But we are a long way off from the capability of applying it to "general input."

The fact is, AI is not going to impress anyone until it can handle an equivalently general class of inputs to what a human can handle. And think about the inputs that humans receive. The five senses are just the beginning. They're the concrete inputs we receive from the world. In addition to them, there's a near infinite class of more abstract inputs that build on top of them. As humans, we interpret social cues, hormonal feedback, the emotions our body often inexplicably generates, and dozens of other "second-order" inputs that are derived from our fundamental sensory ones, but seem equally fundamental to us.

Ok, sure. Maybe you can model the brain, and maybe it can respond to inputs from its sensory environment. But those inputs are the absolute lowest building block of the totality of input into our brain. We have an entire subconscience dedicated to processing those fundamental sensory inputs, and generating derivative ones for our consciousness to process. The difficulty in AI, in 2014, is modeling that subconscience. How do we go from basic environmental input to its infinitely more complex derivatives? How do we build a subconscience that our current AI can interpret as an input of its own?

We are a long way off from this capability. Nobody is going to figure it out on their own, and neither is a company of a few dozen. To me, it seems incredibly wasteful to spend 30 years working on this product without revealing any of the journey. After all this time, what do you have to show for it? Basically nothing. Your AI can apparently complete tasks of a similar complexity to state of the art that's been developed in less than a decade.

I want to see models, and I want the community to discuss them, problem solve with them, and build on them. Enough of this closed source, proprietary, snail's pace AI development.

Software is constrained by computing limitations. Fundamentally, there can be no computational model that even approaches the complexity of a brain. At least not today. So why not forget about trying to build Skynet, and work with the community to further the field together?

[1] www.numenta.org