This is an important paper in the development of neural reasoning capabilities which should reduce the brittleness of purely symbolic approaches: Neural Logic Machine
The potential reasoning capabilities, such as with regard to multi-step inference, as in problem solving and theorem proving, are most interesting, but there are important contemporary applications in machine learning and question answering. I’ll just provide a few hightlights from the paper on the latter and some more points and references on the former below.
Continue reading “Neural Logic Machines”
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:
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”
Text analytics and natural language processing have made tremendous advances in the last few years. Unfortunately, there is a lot more to understanding natural language that TA/NLP.
I was reading a paper today about NLP pipelines for question answering that used machine learning to find what tools are good at what tasks and to configure a pipeline by selecting the best tool for a given task from each of the types of components in the pipeline. The paper has a long list of various components, so I checked a few out. Most of those of interest were available on the web so that they could be easily composed into pipelines without a lot of software setup. Looking at these I quickly tired in disappointment. Here are some of the reasons.
I am not surprised by these results. NLU is hard. But they are not particularly strong results either. I’m surprised that people find such results useful (if they do).
Continue reading “TA/NLP: It’s a jungle out there!”
In preparing for some natural language generation, I came across some work on natural logic and reasoning by textual entailment (RTE) by Richard Bergmair in his PhD at Cambridge:
The work he describes overlaps our approach to robust inference from the deep, variable-precision semantics that result from linguistic analysis and disambiguation using the English Resource Grammar (ERG) and the Linguist™.
Continue reading “Robust Inference and Slacker Semantics”
Thanks to John Sowa‘s comment on LinkedIn for this link which, although slightly dated, contains the following:
In August, I had the chance to speak with Peter Norvig, Director of Google Research, and asked him if he thought that techniques like deep learning could ever solve complicated tasks that are more characteristic of human intelligence, like understanding stories, which is something Norvig used to work on in the nineteen-eighties. Back then, Norvig had written a brilliant review of the previous work on getting machines to understand stories, and fully endorsed an approach that built on classical “symbol-manipulation” techniques. Norvig’s group is now working within Hinton, and Norvig is clearly very interested in seeing what Hinton could come up with. But even Norvig didn’t see how you could build a machine that could understand stories using deep learning alone.
Other quotes along the same lines come from Oren Etzioni in Looking to the Future of Data Science:
- But in his keynote speech on Monday, Oren Etzioni, a prominent computer scientist and chief executive of the recently created Allen Institute for Artificial Intelligence, delivered a call to arms to the assembled data mavens. Don’t be overly influenced, Mr. Etzioni warned, by the “big data tidal wave,” with its emphasis on mining large data sets for correlations, inferences and predictions. The big data approach, he said during his talk and in an interview later, is brimming with short-term commercial opportunity, but he said scientists should set their sights further. “It might be fine if you want to target ads and generate product recommendations,” he said, “but it’s not common sense knowledge.”
- The “big” in big data tends to get all the attention, Mr. Etzioni said, but thorny problems often reside in a seemingly simple sentence or two. He showed the sentence: “The large ball crashed right through the table because it was made of Styrofoam.” He asked, What was made of Styrofoam? The large ball? Or the table? The table, humans will invariably answer. But the question is a conundrum for a software program, Mr. Etzioni explained
- Instead, at the Allen Institute, financed by Microsoft co-founder Paul Allen, Mr. Etzioni is leading a growing team of 30 researchers that is working on systems that move from data to knowledge to theories, and then can reason. The test, he said, is: “Does it combine things it knows to draw conclusions?” This is the step from correlation, probabilities and prediction to a computer system that can understand
This is a significant statement from one of the best people in fact extraction on the planet!
As you know from elsewhere on this blog, I’ve been involved with the precursor to the AIAI (Vulcan’s Project Halo) and am a fan of Watson. But Watson is the best example of what Big Data, Deep Learning, fact extraction, and textual entailment aren’t even close to:
- During a Final Jeopardy! segment that included the “U.S. Cities” category, the clue was: “Its largest airport was named for a World War II hero; its second-largest, for a World War II battle.”
- Watson responded “What is Toronto???,” while contestants Jennings and Rutter correctly answered Chicago — for the city’s O’Hare and Midway airports.
Sure, you can rationalize these things and hope that someday the machine will not need reliable knowledge (or that it will induce enough information with enough certainty). IBM does a lot of this (e.g., see the source of the quotes above). That day may come, but it will happen a lot sooner with curated knowledge.
Look for a forthcoming post on the broader subject of personalized e-learning. In the meantime, here’s a tip on writing good multiple choice questions:
- target wrong answers to diagnose misconceptions.
Better approaches to adaptive education model the learning objectives that are assessed by questions such as the following from the American Academy for the Advancement of Science which goes the extra mile to also model the misconceptions underlying incorrect answers:
Knewton is an interesting company providing a recommendation service for adaptive learning applications. In a recent post, Jonathon Goldman describes an algorithmic approach to generating questions. The approach focuses on improving the manual authoring of test questions (known in the educational realm as “assessment items“). It references work at Microsoft Research on the problem of synthesizing questions for a algebra learning game.
We agree that more automated generation of questions can enrich learning significantly, as has been demonstrated in the Inquire prototype. For information on a better, more broadly applicable approach, see the slides beginning around page 16 in Peter Clark’s invited talk.
What we think is most promising, however, is understanding the reasoning and cognitive skill required to answer questions (i.e., Deep QA). The most automated way to support this is with machine understanding of the content sufficient to answer the questions by proving answers (i.e., multiple choices) right or wrong, as we discuss in this post and this presentation.
Orin Etzioni is a marvelous choice to lead the Allen Institute for AI (aka AI2). The NL/ML path is the right path for scaling up the deep knowledge that Paul Allen’s vision of a Digital Aristotle requires. You can read more about it below and here’s more background on the change in the direction and on some evidence that the path holds great promise.
Going beyond Siri and Watson: Microsoft co-founder Paul Allen taps Oren Etzioni to lead new Artificial Intelligence Institute
Over the last two years, machines have demonstrated their ability to read, listen, and understand English well enough to beat the best at Jeopardy!, answer questions via iPhone, and earn college credit on college advanced placement exams. Today, Google, Microsoft and others are rushing to respond to IBM and Apple with ever more competent artificially intelligent systems that answer questions and support decisions.