Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. At the moment, artificial intelligence may have perfect memories and be better at arithmetic than us, but they are clueless. It takes a few seconds of interaction with any digital assistant to realize one is not in the presence of a very bright interlocutor. Among some of the unexpected items users have found in their shopping lists after talking to (or near) Amazon's Alexa are 150,000 bottles of shampoo, sled dogs, "hunk of poo," and a girlfriend. The mere exasperation of talking to a personal assistant can be enough to miss human companionship, feel nostalgia of all things analog and dumb, and foreswear any future attempts at communicating with mindless pieces of metal inexplicably labelled "smart."
To build a machine that has "common sense" was once a principal goal in the field of artificial intelligence. But most researchers in recent years have retreated from that ambitious aim. Instead, each developed some special technique that could deal with some class of problem well, but does poorly at almost everything else. We are convinced, however, that no one such method will ever turn out to be "best," and that instead, the powerful AI systems of the future will use a diverse array of resources that, together, will deal with a great range of problems. To build a machine that's resourceful enough to have humanlike common sense, we must develop ways to combine the advantages of multiple methods to represent knowledge, multiple ways to make inferences, and multiple ways to learn. We held a two-day symposium in St. Thomas, U.S. Virgin Islands, to discuss such a project -- - to develop new architectural schemes that can bridge between different strategies and representations. This article reports on the events and ideas developed at this meeting and subsequent thoughts by the authors on how to make progress.
Parts of this essay by Andrew Smart are adapted from his book Beyond Zero And One (2015), published by OR Books. Machine intelligence is growing at an increasingly rapid pace. The leading minds on the cutting edge of AI research think that machines with human-level intelligence will likely be realized by the year 2100. Beyond this, artificial intelligences that far outstrip human intelligence would rapidly be created by the human-level AIs. This vastly superhuman AI will result from an "intelligence explosion."
Nearly half a century has passed between the release of the films 2001: A Space Odyssey (1968) and Transcendence (2014), in which a quirky scientist's consciousness is uploaded into a computer. Despite being 50 years apart, their plots, however, are broadly similar. Science fiction stories continue to imagine the arrival of human-like machines that rebel against their creators and gain the upper hand in battle. In the field of artificial intelligence (AI) research, over the last 30 years, progress has been similarly slower than expected. While AI is increasingly part of our everyday lives - in our phones or cars - and computers process large amounts of data, they still lack human-level capacity to make deductions from the information they're given.
This paper summarizes some of the technical background, research ideas, and possible development strategies for achieving machine common sense. Machine common sense has long been a critical-but-missing component of Artificial Intelligence (AI). Recent advances in machine learning have resulted in new AI capabilities, but in all of these applications, machine reasoning is narrow and highly specialized. Developers must carefully train or program systems for every situation. General commonsense reasoning remains elusive. The absence of common sense prevents intelligent systems from understanding their world, behaving reasonably in unforeseen situations, communicating naturally with people, and learning from new experiences. Its absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general, human-like AI systems we would like to build in the future. Machine common sense remains a broad, potentially unbounded problem in AI. There are a wide range of strategies that could be employed to make progress on this difficult challenge. This paper discusses two diverse strategies for focusing development on two different machine commonsense services: (1) a service that learns from experience, like a child, to construct computational models that mimic the core domains of child cognition for objects (intuitive physics), agents (intentional actors), and places (spatial navigation); and (2) service that learns from reading the Web, like a research librarian, to construct a commonsense knowledge repository capable of answering natural language and image-based questions about commonsense phenomena.