common-sense reasoning

Who’s Doing Common-Sense Reasoning And Why It Matters

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Getting Orwell Wrong

Editor’s note: Catherine Havasi is CEO and co-founder of Luminoso, an artificial intelligence-based text analytics company in Cambridge. Luminoso was founded on nearly a decade of research at the MIT Media Lab on how NLP and machine learning could be applied to text analytics. Catherine also directs the Open Mind Common Sense Project, one of the largest common sense knowledge bases in the world, which she co-founded alongside Marvin Minsky and Push Singh in 1999.

Imagine for a moment that you run into a friend on the street after you return from a vacation in Mexico.

“How was your vacation?” your friend asks.

“It was wonderful. We’re so happy with the trip,” you reply. “It wasn’t too humid, though the water was a bit cold.”

No surprises there, right? You and your friend both know that you’re referring to the weather in terms of “humidity” and the ocean in terms of “cold.”

Now imagine you try to have that same conversation with a computer. Your response would be met with something akin to: “Does. Not. Compute.”

Part of the problem is that when we humans communicate, we rely on a vast background of unspoken assumptions. Everyone knows that “water is wet,” and “people want to be happy,” and we assume everyone we meet shares this knowledge. It forms the basis of how we interact and allows us to communicate quickly, efficiently, and with deep meaning.

As advanced as technology is today, its main shortcoming as it becomes a large part of daily life in society is that it does not share these assumptions.

We find ourselves talking more and more to our devices — to our mobile phones and even our televisions. But when we talk to Siri, we often find that the rules that underlie her can’t comprehend exactly what we want if we stray far from simple commands. For this vision to be fulfilled, we’ll need computers to understand us as we talk to each other in a natural environment. For that, we’ll need to continue to develop the field of common-sense reasoning — without it, we’re never going to be able to have an intelligent conversation with Siri, Google Glass or our Xbox.

What Is Common-Sense Reasoning?

Common-sense reasoning is a field of artificial intelligence that aims to help computers understand and interact with people in a more naturally by finding ways to collect these assumptions and teach them to computers. Common Sense Reasoning has been most successful in the field of natural language processing (NLP), though notable work has been done in other areas. This area of machine learning, with its strange name, is starting to quietly infiltrate different applications ranging from text understanding to processing and comprehending what’s in a photo.

Without common sense, it will be difficult to build adaptable and unsupervised NLP systems in an increasingly digital and mobile world. When we talk to each other and talk online, we try to be as interesting as possible and take advantage of new ways to express things. It’s important to create computers that can keep pace with us.

There’s more to it than one would think. If I asked you if a giraffe would fit in your office, you could answer the question quite easily despite the fact that in all probability you had never pictured a giraffe inhabiting your office, quietly munching on your ficus while your favorite Pandora station plays in the background. This is a perfect example of you not just knowing about the world, but knowing how to apply your world knowledge to things you haven’t thought about before.

The power of common sense systems is that they are highly adaptive, adjusting to topics as varied as restaurant reviews, hiking boot surveys, and clinical trials, and doing so with speed and accuracy. This is because we understand new words from the context they are used in. We use common sense to make guesses at word meanings and then refine those guesses and we’ve built a system that works similarly. Additionally, when we understand complex or abstract concepts, it’s possible we do so by making an analogy to a simple concept, a theory described by George Lakoff in his book, “Metaphors We Live By.” The simple concepts are common sense.

There are two major schools of thought in common-sense reasoning. One side works with more logic-like or rule-based representations, while the other uses more associative and analogy-based reasoning or “language-based” common sense — the latter of which draws conclusions that are fuzzier but closer to the way that natural language works.

Whether you realize it or not, you interact with both of these kinds of systems on a daily basis.

You’ve probably heard of IBM’s Watson, which famously won at Jeopardy, but it’s a lesser-known fact that Watson’s predecessor was a project called Cyc that was developed in 1984 by Doug Lenat. The makers of Cyc, called Cycorp, operate a large repository of logic-based common sense facts. It’s still active today and remains one of the largest logic-based common sense projects.

In the school of language-based common sense, the Open Mind Common Sense project was started in 1999 by Marvin Minsky, Push Singh, and myself. OMCS and ConceptNet, its more well-known offshoot, include an information store in plain text, as well as a large knowledge graph. The project became an early success in crowdsourcing, and now ConceptNet contains 17 million facts in many languages.

Why Is It Important Now?

The last few years have seen great steps forward in particular types of machine learning: vector-based machine learning and deep learning. They have been instrumental in advancing language-based common sense, thus bringing computers one step closer to processing language the way humans do.

NLP is where common-sense reasoning excels, and the technology is starting to find its way into commercial products. Though there is still a long way to go, common-sense reasoning will continue to evolve rapidly in the coming years and the technology is stable enough to be in business use today. It holds significant advantages over existing ontology and rule-based systems, or systems based simply on machine learning.

It won’t be long before you have a more common-sense conversation with your computer about your trip to Mexico. And when you tell it that the water was a bit cold, your computer could reply: “I’m sorry to hear the ocean was chilly, it tends to be at this time of year. Though I saw the photos from your trip and it looks like you got to wear that lovely new bathing suit you bought last week.”

IMAGE BY Shutterstock USER NesaCera