The effects of the use of a conversational model and opportunities for reflections in computer-based role playing

This study examined the effects of an instructional program on 21-year-old students' interpersonal skills development (N = 104). The HyperCard 2.1 program ¿Telling bad news¿ could contain a conversational model that informed students about the main moments and actions in conducting a bad-news conversation. In addition, the program could vary the students' opportunities for reflection by slowing down the dialog. It was expected that the conversational-model-present groups and the high reflection groups would show more effective interpersonal skill acquisition, knowledge acquisition, and a more complete understanding of the skill (better tests results) than the conversational-model-absent groups and the low reflection groups. Both elements were found to affect the students' interpersonal skill development. The presence of a conversational model significantly improved the students' role-play, F(1, 94) = 8.79, p < .01, and their performance on the knowledge test, F(1, 94) = 115.28, p < .001. When also given opportunities for reflection, the students' performance in a roleplay and on the knowledge test improved even more, F(4, 91) = 2.69, p < .05. The instruction program with the presence of a conversational model in combination with opportunities for reflection is, therefore, considered as having the potential to assist in realizing effective gradual lead into interpersonal skills learning and instruction for novices

A Conversational Paradigm for Program Synthesis

Program synthesis strives to generate a computer program as a solution to a given problem specification. We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program space and user intent specification faced in prior approaches. Our new approach casts the process of writing a specification and program as a multi-turn conversation between a user and a system. It treats program synthesis as a sequence prediction problem, in which the specification is expressed in natural language and the desired program is conditionally sampled. We train a family of large language models, called CodeGen, on natural language and programming language data. With weak supervision in the data and the scaling up of data size and model size, conversational capacities emerge from the simple autoregressive language modeling. To study the model behavior on conversational program synthesis, we develop a multi-turn programming benchmark (MTPB), where solving each problem requires multi-step synthesis via multi-turn conversation between the user and the model. Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm. In addition, our model CodeGen (with up to 16B parameters trained on TPU-v4) outperforms OpenAI's Codex on the HumanEval benchmark. We make the training library JaxFormer including checkpoints available as open source contribution: https://github.com/salesforce/CodeGen

An assembler for the MOS Technology 6502 microprocessor as implemented in jolt (TM) and KIM-1 (TM)

Design of low-cost, microcomputer-based navigation receivers, and the assembler are described. The development of computer software for microprocessors is materially aided by the assembler program using mnemonic variable names. The flexibility of the environment provided by the IBM's Virtual Machine Facility and the Conversational Monitor System, make possible the convenient assembler access. The implementation of the assembler for the microprocessor chip serves a part of the present need and forms a model for support of other microprocessors

Conversational Exploratory Search via Interactive Storytelling

Conversational interfaces are likely to become more efficient, intuitive and engaging way for human-computer interaction than today's text or touch-based interfaces. Current research efforts concerning conversational interfaces focus primarily on question answering functionality, thereby neglecting support for search activities beyond targeted information lookup. Users engage in exploratory search when they are unfamiliar with the domain of their goal, unsure about the ways to achieve their goals, or unsure about their goals in the first place. Exploratory search is often supported by approaches from information visualization. However, such approaches cannot be directly translated to the setting of conversational search. In this paper we investigate the affordances of interactive storytelling as a tool to enable exploratory search within the framework of a conversational interface. Interactive storytelling provides a way to navigate a document collection in the pace and order a user prefers. In our vision, interactive storytelling is to be coupled with a dialogue-based system that provides verbal explanations and responsive design. We discuss challenges and sketch the research agenda required to put this vision into life.Comment: Accepted at ICTIR'17 Workshop on Search-Oriented Conversational AI (SCAI 2017