No abstract

Argumentation schemes bring artificial intelligence into day to day conversation. Interpreting the force of an utterance, be it an assertion, command, or question, remains a task for achieving this goal. But it is not an easy task. An interpretation of force depends on a speaker’s use of words for a hearer at the moment of utterance. Ascribing force relies on grammatical mood, though not in a straightforward or regular way. We face a dilemma: on one hand, deciding force requires an understanding of the speaker’s words; on the other hand, word meaning may shift given the force in which the words are spoken. A precise theory of how mood and force relate helps us handle this dilemma, which, if met, expands the use of argumentation schemes in language processing. Yet, as our analysis shows, force is an inconstant variable, one that contributes to a scheme’s defeasibility. We propose using critical questions to help us decide the force of utterances.

Human-machine teams or systems are integral parts of society and will likely become more so. Unsettled are the effects of these changes, their mechanism(s), and how to measure them. In this article, I propose a central concept for understanding human-machine interaction: convergent cause. That is, Agent 1’s response to the object is caused by the object and Agent 2’s response, while Agent 2 responds to Agent 1’s response and the object. To the extent a human-machine team acts, AI converges with a human. One benefit of this concept is that it allows degrees, and so avoids the question of Strong or Weak AI. To defend my proposal, I repurpose Donald Davidson’s triangulation as a model for human-machine teams and systems.

No abstract

No abstract