Visual programming has the potential of providing novice programmers with a low-code experience to build customized processing pipelines.Existing systems typically require users to build pipelines from scratch, implying that novice users are expected to set up and link appropriate nodes from a blank workspace.In this paper, we introduce InstructPipe, an AI assistant for prototyping machine learning (ML) pipelines with text instructions.We contribute two large language model (LLM) modules and a code interpreter as part of our framework.The LLM modules generate pseudocode for a target pipeline, and the interpreter renders the pipeline in the node-graph editor for further human-AI collaboration.Both technical and user evaluation (N=16) shows that InstructPipe empowers users to streamline their ML pipeline workfow, reduce their learning curve, and leverage open-ended commands to spark innovative ideas.

In recent years, there has been a proliferation of multimedia applications that leverage machine learning (ML) for interactive experiences. Prototyping ML-based applications is, however, still challenging, given complex workflows that are not ideal for design and experimentation. To better understand these challenges, we conducted a formative study with seven ML practitioners to gather insights about common ML evaluation workflows.

Video conferencing solutions like Zoom, Google Meet, and Microsoft Teams are becoming increasingly popular for facilitating conversations, and recent advancements such as live captioning help people better understand each other. We believe that the addition of visuals based on the context of conversations could further improve comprehension of complex or unfamiliar concepts. To explore the potential of such capabilities, we conducted a formative study through remote interviews (N=10) and crowdsourced a dataset of over 1500 sentence-visual pairs across a wide range of contexts. These insights informed Visual Captions, a real-time system that integrates with a video conferencing platform to enrich verbal communication. Visual Captions leverages a fine-tuned large language model to proactively suggest relevant visuals in open-vocabulary conversations. We present findings from a lab study (N=26) and an in-the-wild case study (N=10), demonstrating how Visual Captions can help improve communication through visual augmentation in various scenarios.