When I wrote Are Vitamins Subject to Sales Tax, I was addressing the process of translating knowledge expressed in formal documents, like laws, regulations, and contracts, into logic suitable for inference using the Linguist.
Recently, one of my favorite researchers working in natural language processing and reasoning, Luke Zettlemoyer, is among the authors of Entailment-driven Extracting and Editing for Conversational
Machine Reading. This is a very nice turn towards knowledge extraction and inference that improves on superficial reasoning by textual entailment (RTE).
I recommend this paper, which relates to BERT, which is among my current favorites in deep learning for NL/QA. Here is an image from the paper, FYI:
This is a great page on language modeling with an awesome graphic and commentary on its learned “sentiment neuron”.
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.
Continue reading “Simply Smarter Intelligent Agents”
We are using statistical techniques to increase the automation of logical and semantic disambiguation, but nothing is easy with natural language.
Here is the Stanford Parser (the probabilistic context-free grammar version) applied to a couple of sentences. There is nothing wrong with the Stanford Parser! It’s state of the art and worthy of respect for what it does well.
Continue reading “Problems with Probabilistic Parsing”
Going on 5 years ago, I wrote part 1. Now, finally, it’s time for the rest of the story.
Continue reading “Confessions of a production rule vendor (part 2)”
This is not all that simple of an article, but it walks you through, from start to finish, how we get from English to logic. In particular, it shows how English sentences can be directly translated into formal logic for use with in automated reasoning with theorem provers, logic programs as simple as Prolog, and even into production rule systems.
There is a section in the middle that is a bit technical about the relationship between full logic and more limited systems (e.g., Prolog or production rule systems). You don’t have to appreciate the details, but we include them to avoid the impression of hand-waving
The examples here are trivial. You can find many and more complex examples throughout Automata’s web site.
Consider the sentence, “A cell has a nucleus.”:
Continue reading “Simply Logical English”
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, deep learning, and deep reasoning. The deep aspects are critical but no more so than knowledge (including “common sense”). Continue reading “Natural Intelligence”
In a prior post we showed how extraordinarily ambiguous, long sentences can be precisely interpreted. Here we take a simpler look upon request.
Let’s take a sentence that has more than 10 parses and configure the software to disambiguate among no more than 10.
Once again, this is a trivial sentence to disambiguate in seconds without iterative parsing!
The immediate results might present:
Suppose the intent is not that the telescope is with my friend, so veto “telescope with my friend” with a right-click.
Continue reading “Iterative Disambiguation”
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.)
Continue reading ““Only full page color ads can run on the back cover of the New York Times Magazine.””