Dramatic success in machine learning has led to a new wave of AI applications (for example, transportation, security, medicine, finance, defense) that offer tremendous benefits but cannot explain ...their decisions and actions to human users. DARPA's explainable artificial intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. Realizing this goal requires methods for learning more explainable models, designing effective explanation interfaces, and understanding the psychologic requirements for effective explanations. The XAI developer teams are addressing the first two challenges by creating ML techniques and developing principles, strategies, and human‐computer interaction techniques for generating effective explanations. Another XAI team is addressing the third challenge by summarizing, extending, and applying psychologic theories of explanation to help the XAI evaluator define a suitable evaluation framework, which the developer teams will use to test their systems. The XAI teams completed the first of this 4‐year program in May 2018. In a series of ongoing evaluations, the developer teams are assessing how well their XAM systems' explanations improve user understanding, user trust, and user task performance.
This issue features expanded versions of articles selected from the 2014 AAAI Conference on Innovative Applications of Artificial Intelligence held in Quebec City, Canada. We present a selection of ...four articles describing deployed applications plus two more articles that discuss work on emerging applications.
This issue features expanded versions of articles selected from the 2015 AAAI Conference on Innovative Applications of Artificial Intelligence held in Austin, Texas. We present a selection of three ...articles describing deployed applications plus two more articles that discuss work on emerging applications.
This issue features expanded versions of articles selected from the 2014 AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI) held in Quebec City, Canada. We present a ...selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications.
In the winter 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question‐answering system called Digital Aristotle. The goal of that first step was to assess the state ...of the art in applied knowledge representation and reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge‐based systems capable of answering questions from that syllabus. This article reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.
The DARPA's Explainable Artificial Intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. This talk ...will summarize the XAI program and present highlights from these Phase 1 evaluations.
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.
PDF
