How do we design an AI system that is intended to act as a communication bridge between two user communities with different mental models and vocabularies? Skillsync is an interactive environment that engages employers (companies) and training providers (colleges) in a sustained dialogue to help them achieve the goal of building a training proposal that successfully meets the needs of the employers and employees. We used a variation of participatory design to elicit requirements for developing AskJill, a question-answering agent that explains how Skillsync works and thus acts as a communication bridge between company and college users. Our study finds that participatory design was useful in guiding the requirements gathering and eliciting user questions for the development of AskJill. Our results also suggest that the two Skillsync user communities perceived glossary assistance as a key feature that AskJill needs to offer, and they would benefit from such a shared vocabulary.
Machine Teaching (MT) is an interactive process where a human and a machine interact with the goal of training a machine learning model (ML) for a specified task. The human teacher communicates their task expertise and the machine student gathers the required data and knowledge to produce an ML model. MT systems are developed to jointly minimize the time spent on teaching and the learner's error rate. The design of human-AI interaction in an MT system not only impacts the teaching efficiency, but also indirectly influences the ML performance by affecting the teaching quality. In this paper, we build upon our previous work where we proposed an MT framework with three components, viz., the teaching interface, the machine learner, and the knowledge base, and focus on the human-AI interaction design involved in realizing the teaching interface. We outline design decisions that need to be addressed in developing an MT system beginning from an ML task. The paper follows the Socratic method entailing a dialogue between a curious student and a wise teacher.
Deep neural network based face recognition models have been shown to be vulnerable to adversarial examples. However, many of the past attacks require the adversary to solve an input-dependent optimization problem using gradient descent which makes the attack impractical in real-time. These adversarial examples are also tightly coupled to the attacked model and are not as successful in transferring to different models. In this work, we propose ReFace, a real-time, highly-transferable attack on face recognition models based on Adversarial Transformation Networks (ATNs). ATNs model adversarial example generation as a feed-forward neural network. We find that the white-box attack success rate of a pure U-Net ATN falls substantially short of gradient-based attacks like PGD on large face recognition datasets. We therefore propose a new architecture for ATNs that closes this gap while maintaining a 10000x speedup over PGD. Furthermore, we find that at a given perturbation magnitude, our ATN adversarial perturbations are more effective in transferring to new face recognition models than PGD. ReFace attacks can successfully deceive commercial face recognition services in a transfer attack setting and reduce face identification accuracy from 82% to 16.4% for AWS SearchFaces API and Azure face verification accuracy from 91% to 50.1%.
We describe a stance towards the generation of explanations in AI agents that is both human-centered and design-based. We collect questions about the working of an AI agent through participatory design by focus groups. We capture an agent's design through a Task-Method-Knowledge model that explicitly specifies the agent's tasks and goals, as well as the mechanisms, knowledge and vocabulary it uses for accomplishing the tasks. We illustrate our approach through the generation of explanations in Skillsync, an AI agent that links companies and colleges for worker upskilling and reskilling. In particular, we embed a question-answering agent called AskJill in Skillsync, where AskJill contains a TMK model of Skillsync's design. AskJill presently answers human-generated questions about Skillsync's tasks and vocabulary, and thereby helps explain how it produces its recommendations.
Machine Teaching (MT) is an interactive process where humans train a machine learning model by playing the role of a teacher. The process of designing an MT system involves decisions that can impact both efficiency of human teachers and performance of machine learners. Previous research has proposed and evaluated specific MT systems but there is limited discussion on a general framework for designing them. We propose a framework for designing MT systems and also detail a system for the text classification problem as a specific instance. Our framework focuses on three components i.e. teaching interface, machine learner, and knowledge base; and their relations describe how each component can benefit the others. Our preliminary experiments show how MT systems can reduce both human teaching time and machine learner error rate.
Building AI agents can be costly. Consider a question answering agent such as Jill Watson that automatically answers students' questions on the discussion forums of online classes based on their syllabi and other course materials. Training a Jill on the syllabus of a new online class can take a hundred hours or more. Machine teaching - interactive teaching of an AI agent using synthetic data sets - can reduce the training time because it combines the advantages of knowledge-based AI, machine learning using large data sets, and interactive human-in-loop training. We describe Agent Smith, an interactive machine teaching agent that reduces the time taken to train a Jill for a new online class by an order of magnitude.
Interpreting the learning dynamics of neural networks can provide useful insights into how networks learn and the development of better training and design approaches. We present an approach to interpret learning in neural networks by measuring relative weight change on a per layer basis and dynamically aggregating emerging trends through combination of dimensionality reduction and clustering which allows us to scale to very deep networks. We use this approach to investigate learning in the context of vision tasks across a variety of state-of-the-art networks and provide insights into the learning behavior of these networks, including how task complexity affects layer-wise learning in deeper layers of networks.
Understanding the per-layer learning dynamics of deep neural networks is of significant interest as it may provide insights into how neural networks learn and the potential for better training regimens. We investigate learning in Deep Convolutional Neural Networks (CNNs) by measuring the relative weight change of layers while training. Several interesting trends emerge in a variety of CNN architectures across various computer vision classification tasks, including the overall increase in relative weight change of later layers as compared to earlier ones.
Autonomous spacecraft maneuver planning using an evolutionary algorithmic approach is investigated. Simulated spacecraft were placed into four different initial orbits. Each was allowed a string of thirty delta-v impulse maneuvers in six cartesian directions, the positive and negative x, y and z directions. The goal of the spacecraft maneuver string was to, starting from some non-polar starting orbit, place the spacecraft into a polar, low eccentricity orbit. A genetic algorithm was implemented, using a mating, fitness, mutation and crossover scheme for impulse strings. The genetic algorithm was successfully able to produce this result for all the starting orbits. Performance and future work is also discussed.