The dream of thinking machines goes back centuries, at least to Gottfried Wilhelm Leibniz, in the 17th century. Leibniz (right) helped invent mechanical calculators, independently of Isaac Newton developed the integral calculus, and had a lifelong fascination with reducing thinking to calculation. His Mathesis Universalis was a vision of universal science made possible by a mathematical language more precise than natural languages, like English. The Limits of Modern AI: A Story In the 18th Century the Enlightenment philosopher and proto-psychologist Étienne Bonnot de Condillac imagined a statue outwardly appearing like a man and also with what he called "the inward organization." In an example of supreme armchair speculation, Condillac imagined pouring facts--bits of knowledge--into its head, wondering when intelligence would emerge. Condillac's musings drew inspiration from the early mechanical philosophy of Thomas Hobbes, who had famously declared that thinking was nothing but ...

Dataset from the U.S. Department of Education that includes various metrics on outcomes from degree-granting undergraduate institutions from 1996-2015, including student debt, college completion rates, job placement, and more

After a chatbot pretending to be a 13-year-old named Eugene Goostman "passed" a Turing test a few years ago, experts in artificial intelligence got together and decided that a traditional Turing test might not be all that effective in measuring the intelligence of a computer program after all. Instead, they came up with (among many other things) the Winograd Schema Challenge, which is intended to determine how well an artificial intelligence system handles commonsense reasoning: understanding the basics about how the world works, and implementing that knowledge in useful and accurate ways. A few weeks ago, the very first Winograd Schema Challenge took place at the International Joint Conference on Artificial Intelligence in New York City. We spoke with Charlie Ortiz, director of the Laboratory for AI and Natural Language Processing at Nuance Communications and one of the organizers of the Winograd Schema Challenge, about how things went, why the challenge is important, and what it means for the future of AI. The Winograd Schema Challenge tasks computer programs with answering a specific type of simple, commonsense question called a pronoun disambiguation problem (PDP).

Apple fixed this error shortly after its virtual assistant was first released in 2011. But a new contest shows that computers still lack the common sense required to avoid such embarrassing mix-ups. The results of the contest were presented at an academic conference in New York this week, and they provide some measure of how much work needs to be done to make computers truly intelligent. 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.

THE WINOGRAD Schema Challenge is a competition intended to reward technologists who can build a system that understands the kind of ambiguous sentences humans come out with all the time, but which are simple for other humans, even stupid ones, to understand. Get it right 90 per cent of the time and 25,000 is up for grabs. And with things like Apple's Siri, Microsoft's Cortana and Google Assistant, the Winograd Schema Challenge must surely be as good as obsolete by now. The best two entrants at the event this week achieved correct scores only 48 per cent of the time, little better than randomly guessing the meaning of the sentences they were supposed to crack. This is despite a decade of advances in the field of artificial intelligence (AI), which has barely shifted since the late 1950s, according to some.

Apple fixed this error shortly after its virtual assistant was first released in 2011. But a new contest shows that computers still lack the common sense required to avoid such embarrassing mix-ups. The results of the contest were presented at an academic conference in New York this week, and they provide some measure of how much work needs to be done to make computers truly intelligent. 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.

As a young researcher tasked with pioneering AI in my corporate research lab, this is an exciting opportunity. We are at the very peak of the AI hype curve. This is no ordinary academic conference. The Japanese had announced their 5th Generation computing project that promised fundamental logical processing. The USA responded with a 10 year research project CyC to give computers common sense reasoning power.

Commonsense knowledge representation and reasoning is key for tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In this paper, we introduce a novel 3D game engine for commonsense knowledge acquisition (GECKA3D) which aims to collect commonsense from game designers through the development of serious games. GECKA3D integrates the potential of serious games and games with a purpose. This provides a platform for the acquisition of re-usable and multi-purpose knowledge, and also enables the development of games that can provide entertainment value and teach players something meaningful about the actual world they live in.

The ability to infer intentions, emotions, and other unobservable psychological states from people's behavior is a hallmark of human social cognition, and an essential capability for future Artificial Intelligence systems. The commonsense theories of psychology and sociology necessary for such inferences have been a focus of logic-based knowledge representation research, but have been difficult to employ in robust automated reasoning architectures. In this paper we model behavior interpretation as a process of logical abduction, where the reasoning task is to identify the most probable set of assumptions that logically entail the observable behavior of others, given commonsense theories of psychology and sociology. We evaluate our approach using Triangle-COPA, a benchmark suite of 100 challenge problems based on an early social psychology experiment by Fritz Heider and Marianne Simmel. Commonsense knowledge of actions, social relationships, intentions, and emotions are encoded as defeasible axioms in first-order logic. We identify sets of assumptions that logically entail observed behaviors by backchaining with these axioms to a given depth, and order these sets by their joint probability assuming conditional independence. Our approach solves almost all (91) of the 100 questions in Triangle-COPA, and demonstrates a promising approach to robust behavior interpretation that integrates both logical and probabilistic reasoning.

Autonomous systems must consider the moral ramifications of their actions. Moral norms vary among people and depend on common sense, posing a challenge for encoding them explicitly in a system. I propose to develop a model of repeated analogical chaining and analogical reasoning to enable autonomous agents to interactively learn to apply common sense and model an individual’s moral norms.