Club guessing and the universal models

We survey the use of club guessing and other pcf constructs in the context of showing that a given partially ordered class of objects does not have a largest, or a universal element

Investigating social interaction strategies for bootstrapping lexicon development

This paper investigates how different modes of social interactions influence the bootstrapping and evolution of lexicons. This is done by comparing three language game models that differ in the type of social interactions they use. The simulations show that the language games which use either joint attention or corrective feedback as a source of contextual input are better capable of bootstrapping a lexicon than the game without such directed interactions. The simulation of the latter game, however, does show that it is possible to develop a lexicon without using directed input when the lexicon is transmitted from generation to generation

Club-guessing, stationary reflection, and coloring theorems

We obtain strong coloring theorems at successors of singular cardinals from failures of certain instances of simultaneous reflection of stationary sets. Along the way, we establish new results in club-guessing and in the general theory of ideals.Comment: Initial public versio

Evaluating Visual Conversational Agents via Cooperative Human-AI Games

As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. In this work, we design a cooperative game - GuessWhich - to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call ALICE, is provided an image which is unseen by the human. Following a brief description of the image, the human questions ALICE about this secret image to identify it from a fixed pool of images. We measure performance of the human-ALICE team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with ALICE. We compare performance of the human-ALICE teams for two versions of ALICE. Our human studies suggest a counterintuitive trend - that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.Comment: HCOMP 201

Knowledge of Persons

What is knowledge of persons, and what is knowing persons like? my answer combines Wittgenstein’s epistemology with levinas’s phenomenology. It says that our knowledge of persons is a hinge proposition for us. And it says that what this knowledge consists in is the experience that levinas calls ”the face to face’: direct and unmediated encounter between persons. As levinas says, for there to be persons at all there has, first, to be a relationship, language, and this same encounter: ”the face to face’ comes first, the existence of individual persons only second. I explore some consequences of this conception for how we think about personhood, and also for how we read Descartes and Augustine