Commonsense Reasoning


AI Common Sense Reasoning

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Today's machine learning systems are more advanced than ever, capable of automating increasingly complex tasks and serving as a critical tool for human operators. Despite recent advances, however, a critical component of Artificial Intelligence (AI) remains just out of reach – machine common sense. Defined as "the basic ability to perceive, understand, and judge things that are shared by nearly all people and can be reasonably expected of nearly all people without need for debate," common sense forms a critical foundation for how humans interact with the world around them. Possessing this essential background knowledge could significantly advance the symbiotic partnership between humans and machines. But articulating and encoding this obscure-but-pervasive capability is no easy feat.


On the Winograd Schema Challenge: Levels of Language Understanding and the Phenomenon of the Missing Text

arXiv.org Artificial Intelligence

The Winograd Schema (WS) challenge has been proposed as an alternative to the Turing Test as a test for machine intelligence. In this short paper we "situate" the WS challenge in the data-information-knowledge continuum, suggesting in the process what a good WS is. Furthermore, we suggest that the WS is a special case of a more general phenomenon in language understanding, namely the phenomenon of the "missing text". In particular, we will argue that what we usually call thinking in the process of language understanding almost always involves discovering the missing text - text is rarely explicitly stated but is implicitly assumed as shared background knowledge. We therefore suggest extending the WS challenge to include tests beyond those involving reference resolution, including examples that require discovering the missing text in situations that are usually treated in computational linguistics under different labels, such as metonymy, quantifier scope ambiguity, lexical disambiguation, and co-predication, to name a few.


A Simple Machine Learning Method for Commonsense Reasoning? A Short Commentary on Trinh & Le (2018)

arXiv.org Artificial Intelligence

Is there a'Simple' Machine Learning Method for Commonsense Reasoning? Menlo Park, CA This is a short Commentary on Trinh & Le (2018) ("A Simple Method for Commonsense Reasoning") that outlines three serious flaws in the cited paper and discusses why data-driven approaches cannot be considered as serious models for the commonsense reasoning needed in natural language understanding in general, and in reference resolution, in particular. A program is then asked the question "what was too small" as a followup to (1a), and the question "what was too big" as a followup to (1b). In a recent paper Trinh and Le (2018) - henceforth T&L - suggested that they have successfully formulated a „simple‟ machine learning method for performing commonsense reasoning, and in particular, the kind of reasoning that would be required in the process of language understanding. In simple terms, T&L suggest the following method for "learning" how to successfully resolve the reference "it" in sentences such as those in (1): generate two The Winograd Schema challenge was named after Terry Winograd, one of the pioneers of AI, who pointed out (Winograd, 1972) the need for using commonsense knowledge in resolving a reference such as „they‟ in sentences such as the following: The city councilmen refused the demonstrators a permit because they a.


Logical Semantics and Commonsense Knowledge: Where Did we Go Wrong, and How to Go Forward, Again

arXiv.org Artificial Intelligence

We argue that logical semantics might have faltered due to its failure in distinguishing between two fundamentally very different types of concepts: ontological concepts, that should be types in a strongly-typed ontology, and logical concepts, that are predicates corresponding to properties of and relations between objects of various ontological types. We will then show that accounting for these differences amounts to the integration of lexical and compositional semantics in one coherent framework, and to an embedding in our logical semantics of a strongly-typed ontology that reflects our commonsense view of the world and the way we talk about it in ordinary language. We will show that in such a framework a number of challenges in natural language semantics can be adequately and systematically treated.


Facebook's AI arm explains its investment in robotics ZDNet

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Facebook on Tuesday officially announced that it's hired some of academia's top AI researchers, defending its practice of drawing talent from universities around the globe. Facebook AI Research (FAIR) "relies on open partnerships to help drive AI forward, where researchers have the freedom to control their own agenda," Facebook Chief AI Scientist Yann LeCun wrote in a blog post. "Ours frequently collaborate with academics from other institutions, and we often provide financial and hardware resources to specific universities. The latest hires include Carnegie Mellon Prof. Jessica Hodgins, who will lead a new FAIR lab in Pittsburgh focused on robotics, large-scale and lifelong learning, common sense reasoning, and AI in support of creativity. She'll be joined by Carnegie Mellon Prof. Abhinav Gupta, another robotics expert.


Facebook's AI arm explains its investment in robotics

ZDNet

Newell-Simon Hall at Carnegie Mellon University is home to the Robotics Institute and the Human-Computer Interaction Institute. Facebook on Tuesday officially announced that it's hired some of academia's top AI researchers, defending its practice of drawing talent from universities around the globe. Facebook AI Research (FAIR) "relies on open partnerships to help drive AI forward, where researchers have the freedom to control their own agenda," Facebook Chief AI Scientist Yann LeCun wrote in a blog post. "Ours frequently collaborate with academics from other institutions, and we often provide financial and hardware resources to specific universities. The latest hires include Carnegie Mellon Prof. Jessica Hodgins, who will lead a new FAIR lab in Pittsburgh focused on robotics, large-scale and lifelong learning, common sense reasoning, and AI in support of creativity.


A Simple Method for Commonsense Reasoning

arXiv.org Artificial Intelligence

Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset~\cite{levesque2011winograd}. In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge.


How could businesses use AI in the future?

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"We know it's going to impact how we run our business and it's certainly going to impact our customers. Dr Adrian Weller, programme director of AI at the Alan Turing Institute, is also enthusiastic for what the future holds. "Looking ahead, there are many exciting technical challenges that could really help us take AI to the next level," he says. "A grand challenge will be how we can try to introduce common sense reasoning to enable a whole host of new applications." "I think we will be challenged in terms of the traditional ways of doing things, and these technologies will open up opportunities for us to experiment," he says.