Читать книгу The Innovation Ultimatum - Steve Brown - Страница 47

AIs are trained to understand something about the world. Typical AIs operate within a bubble. They have no understanding of the way the world works. A lack of common sense limits their abilities. A household robot, on a search to find my reading glasses, should know that my desk and nightstand are good places to look first, and not inside the freezer.

Several organizations are trying to build AIs with common sense. They are building vast databases of the commonsense notions humans use to help them make high-quality decisions. For example, oranges are sweet, but lemons are sour. A tiger won't fit in a shoe box. Water is wet. Oil is viscous. If you overfeed a hamster, it will get fat. We often take this context for granted, but to an AI these notions are not obvious.

Researchers at the Allen Institute crowdsource commonsense insight using Amazon's Mechanical Turk platform. They use machine learning and statistical analysis to extract additional insights and understand the spatial, physical, emotional, and other relationships between things. For example, from a commonsense notion that “A girl ate a cookie,” the system deduces that a cookie is a type of food and that a girl is bigger than a cookie. Allen Institute researchers estimate they need about a million human-sourced pieces of common sense to train their AIs.

The Cyc project, the world's longest-running AI project, takes a different approach. Since 1984, Doug Lenat and his team have hand coded more than 25 million pieces of commonsense knowledge in machine-usable form. Cyc knows things like “Every tree is a plant” and “Every plant dies eventually.” From those pieces of information, it can deduce that every tree will die. Cycorp Company, Cyc's current developers, claims that half of the top 15 companies in the world use Cyc under license. Cyc is used in financial services, healthcare, energy, customer experience, the military, and intelligence.

As they mature, commonsense knowledge systems may help future AIs to answer more complex questions and assist humans in more meaningful ways.