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Simply Smarter Intelligent Agents

Deep learning can produce some impressive chatbots, but they are hardly intelligent.  In fact, they are precisely ignorant in that they do not think or know anything.

More intelligent dialog with an artificially intelligent agent involves both knowledge and thinking.  In this article, we educate an intelligent agent that reasons to answer questions.

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Confessions of a production rule vendor (part 2)

Going on 5 years ago, I wrote part 1.  Now, finally, it’s time for the rest of the story.

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Natural Intelligence

Deep natural language understanding (NLU) is different than deep learning, as is deep reasoning.  Deep learning facilities deep NLP and will facilitate deeper reasoning, but it’s deep NLP for knowledge acquisition and question answering that seems most critical for general AI.  If that’s the case, we might call such general AI, “natural intelligence”.

Deep learning on its own delivers only the most shallow reasoning and embarrasses itself due to its lack of “common sense” (or any knowledge at all, for that matter!).  DARPA, the Allen Institute, and deep learning experts have come to their senses about the limits of deep learning with regard to general AI.

General artificial intelligence requires all of it: deep natural language understanding[1], deep learning, and deep reasoning.  The deep aspects are critical but no more so than knowledge (including “common sense”).[2] (more…)

“Only full page color ads can run on the back cover of the New York Times Magazine.”

A decade or so ago, we were debating how to educate Paul Allen’s artificial intelligence in a meeting at Vulcan headquarters in Seattle with researchers from IBM, Cycorp, SRI,  and other places.

We were talking about how to “engineer knowledge” from textbooks into formal systems like Cyc or Vulcan’s SILK inference engine (which we were developing at the time).   Although some progress had been made in prior years, the onus of acquiring knowledge using SRI’s Aura remained too high and the reasoning capabilities that resulted from Aura, which targeted University of Texas’ Knowledge Machine, were too limited to achieve Paul’s objective of a Digital Aristotle.  Unfortunately, this failure ultimately led to the end of Project Halo and the beginning of the Aristo project under Oren Etzioni’s leadership at the Allen Institute for Artificial Intelligence.

At that meeting, I brought up the idea of simply translating English into logic, as my former product called “Authorete” did.  (We renamed it before Haley Systems was acquired by Oracle, prior to the meeting.)

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Nice AI Video

I found the following video in a recent post by Steve DeAngelis  of Enterra Solutions:

It’s a bit too far towards the singularity/general AI end of the spectrum for me, but nicely done and fun for many not in the field, perhaps:

Enterra is an interesting company,too, FYI.  They are in cognitive computing with a bunch of people formerly of Inference Corporation where I was Chief Scientist.  Doug Lenat of Cycorp was one of our scientific advisors.  Interestingly enough, Enterra uses Cyc!

Deep QA

Our efforts at acquiring deep knowledge from a college biology text have enabled us to answer a number of questions that are beyond what has been previously demonstrated.

For example, we’re answering questions like:

  1. Are the passage ways provided by channel proteins hydrophilic or hydrophobic?
  2. Will a blood cell in a hypertonic environment burst?
  3. If a Paramecium swims from a hypotonic environment to an isotonic environment, will its contractile vacuole become more active?

A couple of these are at higher levels on the Bloom scale of cognitive skills than Watson can reach (which is significantly higher than search engines).

As some other posts have shown in images, we can translate completely natural sentences into formal logic.  We actually do the reasoning using Vulcan’s SILK, which has great capabilities, including defeasibility.  We can also output to RIF or SBVR, but the temporal aspects and various things such as modality and the need for defeasibility favor SILK or Cyc for the best reasoning and QA performance.

One thing in particular is worth noting:  this approach does better with causality and temporal logic than is typically considered by most controlled natural language systems, whether they are translating to a business rules engine or a logic formalism, such as first order or description logic.  The approach promises better application development and knowledge management capabilities for more of the business process management and complex event processing markets.

Cyc is more than encyclopedic

I had the pleasure of visiting with some fine folks at Cycorp in Austin, Texas recently.  Cycorp is interesting for many reasons, but chiefly because they have expended more effort developing a deeper model of common world knowledge than any other group on the planet.  They are different from current semantic web startups.  Unlike Metaweb‘s Freebase, for example, Cycorp is defining the common sense logic of the world, not just populating databases (which is an unjust simplification of what Freebase is doing, but is proportionally fair when comparing their ontological schemata to Cyc’s knowledge).  Not only does Cyc have the largest and most practical ontology on earth, they have almost incomprehensible numbers of formulas[1] describing the world.   (more…)

Over $100m in 12 months backs natural language for the semantic web

Radar Networks is accelerating down the path towards the world’s largest body of knowledge about what people care about using Twine to organize their bookmarks.  Unlike social bookmarking sites, Twine uses natural language processing technology to read and categorize people’s bookmarks in a substantial ontology.  Using this ontology, Twine not only organizes their bookmarks intelligently but also facilitates social networking and collaborative filtering that result in more relevant suggestions of others’ bookmarks than other social bookmarking sites can provide.

Twine should rapidly eclipse social bookmarking sites, like Digg and Redditt.  This is no small feat!

The underlying capabilities of Twine present Radar Networks with many other opportunities, too.  Twine could spider out from bookmarks and become a general competitor to Google, as Powerset hopes to become.  Twine could become the semantic web’s Wikipedia, to which Metaweb’s Freebase aspires. (more…)