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This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities in an AI system by embedding these causal components. We hope that the high-level description of our vision included in this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks, and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence.
Managing inputs that are novel, unknown, or out-of-distribution is critical as an agent moves from the lab to the open world. Novelty-related problems include being tolerant to novel perturbations of the normal input, detecting when the input includes novel items, and adapting to novel inputs. While significant research has been undertaken in these areas, a noticeable gap exists in the lack of a formalized definition of novelty that transcends problem domains. As a team of researchers spanning multiple research groups and different domains, we have seen, first hand, the difficulties that arise from ill-specified novelty problems, as well as inconsistent definitions and terminology. Therefore, we present the first unified framework for formal theories of novelty and use the framework to formally define a family of novelty types. Our framework can be applied across a wide range of domains, from symbolic AI to reinforcement learning, and beyond to open world image recognition. Thus, it can be used to help kick-start new research efforts and accelerate ongoing work on these important novelty-related problems.
The ability to learn and reason with causal knowledge is a key aspect of intelligent behavior. In contrast to mere statistical association, knowledge of causation enables reasoning about the effects of actions. Causal reasoning is vital for autonomous agents and for a range of applications in science, medicine, business, and government. However, current methods for causal inference are hobbled because they use relatively inexpressive models. Surprisingly, current causal models eschew nearly every major representational innovation common in a range of other fields both inside and outside of computer science, including representation of objects, relationships, time, space, and hierarchy. Even more surprisingly, a range of recent research provides strong evidence that more expressive representations make possible causal inferences that are otherwise impossible and remove key biases that would otherwise afflict more naive inferences. New research on causal inference should target increases in expressiveness to improve accuracy and effectiveness.
Dialogue systems, also called chatbots, are now used in a wide range of applications. However, they still have some major weaknesses. One key weakness is that they are typically trained from manually-labeled data and/or written with handcrafted rules, and their knowledge bases (KBs) are also compiled by human experts. Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs. Thus, the level of user satisfactory is often low. In this paper, we propose to dramatically improve the situation by endowing the chatbots the ability to continually learn (1) new world knowledge, (2) new language expressions to ground them to actions, and (3) new conversational skills, during conversation by themselves so that as they chat more and more with users, they become more and more knowledgeable and are better and better able to understand diverse natural language expressions and to improve their conversational skills.
The prevalence and success of AI applications have been tempered by concerns about the controllability of AI systems about AI's impact on the future of work. These concerns reflect two aspects of a central question: how would humans work with AI systems? While research on AI safety focuses on designing AI systems that allow humans to safely instruct and control AI systems, research on AI and the future of work focuses on the impact of AI on humans who may be unable to do so. This Blue Sky Ideas paper proposes a unifying set of declarative principles that enable a more uniform evaluation of arbitrary AI systems along multiple dimensions of the extent to which they are suitable for use by specific classes of human operators. It leverages recent AI research and the unique strengths of the field to develop human-centric principles for AI systems that address the concerns noted above.
Game theory is typically used to model the interaction among (software) agents in multiagent systems and, therefore, is a key topic at leading AI conferences. Game-theoretic models, however, are often based on the assumption that agents are perfectly rational and narrowly selfish and are interested only in maximizing their own gains, no matter what the costs to the other agents are. This summary paper presents various ways of introducing certain notions of altruism into existing game-theoretic models in both noncooperative and cooperative games, in the hope that simulating altruistic behavior in AI systems will make AI better suit real-world applications—and thus may make the real world a better place.
Dialogue systems powered by conversational artificial intelligence (AI) have never been so popular. Interacting with computer through languages reveals a more natural interface to give orders and acquire information---just like human communication. Due to promising potential as virtual assistants and/or social bots, major NLP, AI and even Search & Mining communities are explicitly calling-out for contributions of conversational studies. Learning towards real conversational intelligence is a trip to Mars; perhaps we are yet on Earth. We have achieved substantial progress from recent research outputs. Still we have major obstacles to overcome. In this paper, we present an overview of progress and look forward to future trends so as to shed light on possible directions towards success.