Knowledge that Everyone Knows. "People do not walk on their heads." The assertion comes about 900 statements deep into the 527,308 items that comprise the Open Mind common sense database. It's after "Laws are the rules of society" and before "The sky is blue during the day." This collection of mundane facts, which would take more than 20,000 pages to print out, consists entirely of statements so unremarkable they are barely worth stating. Most of us would correctly dismiss them as common sense.
– from D.C. Denison, Guess who's smarter. Boston Globe Online (page hosted at MIT), May 26, 2003.
Coreference resolution is a very challenging NLP task in which you try to link mentions with real life entities. It is the basis of the Winograd Schema Challenge, a test designed to defeat the AIs who've beaten the Turing Test! Hope you like it, I definitely think there should be more interactive demo of NLP systems like this!
It's also home to Alexa, the voice assistant which powers the $179 Echo and Echo dot gadgets. Amazon's machine learning boss (and founder of Amazon Research Cambridge) Professor Neil Lawrence, yesterday discussed the ethics of using our voices to train computers. But when quizzed on whether new starters would be offered specific ethics training by the Sun, Lawrence said that those in control of Amazon's machines were only trained in "information security." "The problems we solve in the Alexa Knowledge team in Cambridge help Alexa get smarter by understanding the different ways people talk, by learning more and more facts about the world, by improving her common sense reasoning and by responding in the most natural way possible in multiple languages."
In order for AI systems to enhance quality of life, both personally and professionally, they must acquire broad and deep knowledge from multiple domains, learn continuously from interactions with people and environments, and support reasoned decisions. In order for AI systems to enhance humans' quality of life, both personally and professionally, they must acquire broad and deep knowledge from multiple domains, learn continuously from interactions with people and environments, and support reasoned decisions. In particular, unsupervised learning capabilities are needed to provide AI systems with common sense reasoning, methods should be developed to avoid bias and specificity in data sets, AI algorithms should be transparent and interpretable, and should be able to interact with humans in natural ways. The AI field's long-term progress depend upon many advances, including the following ones: Machine learning and reasoning: Most current AI systems use supervised learning, using massive amounts of labeled data for training.
Artificial Intelligence (which I'll refer to hereafter by its nickname, "AI") is the subfield of Computer Science devoted to developing programs that enable computers to display behavior that can (broadly) be characterized as intelligent. But substantial interest remains in the long-range goal of building generally intelligent, autonomous agents, even if the goal of fully human-like intelligence is elusive and is seldom pursued explicitly and as such. Although logic in AI grew out of philosophical logic, in its new setting it has produced new theories and ambitious programs that would not have been possible outside of a community devoted to building full-scale computational models of rational agency. Antonelli 2012a is a good entry point for readers interested in nonmonotonic logic, and Shanahan 2009a is a useful discussion of the frame problem.
The Winograd Schema Challenge asks computers to make sense of sentences that are ambiguous but usually simple for humans to parse. Disambiguating Winograd Schema sentences requires some common-sense understanding. Marcus, who is also the cofounder of a new AI startup, Geometric Intelligence, says it's notable that Google and Facebook did not take part in the event, even though researchers at these companies have suggested they are making major progress in natural language understanding. "It's going to come up when you start to support dialogues," says Charlie Ortiz, a senior principal researcher at Nuance, a company that makes voice recognition and voice interface software, which sponsored the Winograd Schema Challenge.
Endowing computers with common sense is one of the major long-term goals of Artificial Intelligence research. One approach to this problem is to formalize commonsense reasoning using representations based on formal logic or other formal representations. The challenges to creating such a formalization include the accumulation of large amounts of knowledge about our everyday world, the representation of this knowledge in suitable formal languages, the integration of different representations in a coherent way, and the development of reasoning methods that use these representations.
That's because computers are playing increasingly important roles in so many aspects of our lives. Part of the problem is that most machine learning systems don't combine reasoning with calculations. By adding reasoning to machine learning systems correlations and insights become much more useful. "Common-sense reasoning is a field of artificial intelligence that aims to help computers understand and interact with people more naturally by finding ways to collect these assumptions and teach them to computers.
At the Machine Intelligence Summit in Berlin last week, Jeremy presented advances in mobile robot task planning and manipulation, with an overview of the field and examples of work from his lab, including machine vision, common sense reasoning and robotic grasping. This includes methods for task planning, manipulation, long life robots, whole body control, machine vision, and machine learning. I would also expect robot grasping in unstructured settings, such as logistics picking, to be solved, though not necessarily with the speed and reliability of humans. Jeremy Wyatt spoke at the Machine Intelligence Summit, Berlin, on 29-30 June.