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A Design-Stage-Oriented Framework to Introduce Artificial Intelligence and Machine Learning in Design Education
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Large language models that exhibit instruction-following behaviour represent one of the biggest recent upheavals in conversational interfaces, a trend in large part fuelled by the release of OpenAI’s ChatGPT, a proprietary large language model for text generation fine-tuned through reinforcement learning from human feedback (LLM+RLHF). We review the risks of relying on proprietary software and survey the first crop of open-source projects of comparable architecture and functionality. The main contribution of this paper is to show that openness is differentiated, and to offer scientific documentation of degrees of openness in this fast-moving field. We evaluate projects in terms of openness of code, training data, model weights, RLHF data, licensing, scientific documentation, and access methods. We find that while there is a fast-growing list of projects billing themselves as ‘open source’, many inherit undocumented data of dubious legality, few share the all-important instruction-tuning (a key site where human labour is involved), and careful scientific documentation is exceedingly rare. Degrees of openness are relevant to fairness and accountability at all points, from data collection and curation to model architecture, and from training and fine-tuning to release and deployment. [See also: https://opening-up-chatgpt.github.io ]
This paper presents the “CDAC AI life cycle,” a comprehensive life cycle for the design, development, and deployment of artificial intelligence (AI) systems and solutions. It addresses the void of a practical and inclusive approach that spans beyond the technical constructs to also focus on the challenges of risk analysis of AI adoption, transferability of prebuilt models, increasing importance of ethics and governance, and the composition, skills, and knowledge of an AI team required for successful completion. The life cycle is presented as the progression of an AI solution through its distinct phases—design, develop, and deploy—and 19 constituent stages from conception to production as applicable to any AI initiative. This life cycle addresses several critical gaps in the literature where related work on approaches and methodologies are adapted and not designed specifically for AI. A technical and organizational taxonomy that synthesizes the functional value of AI is a further contribution of this article.
Recent advances in Generative Adversarial Networks (GANs) hold considerable promise in architecture, especially in the early, creative stages of design. However, while GANs are capable of producing infinite numbers of new designs based on a given dataset, the architectural relevance and meaningfulness of the results have been questionable. This paper presents an experimental research method to examine how human and artificial intelligences can inform each other to generate new designs that are culturally and architecturally meaningful. The paper contributes to our understanding of GANs in architecture by describing the nuances of different GAN models (SAGAN vs DCGAN) for the generation of new designs, and the use of Natural Language Processing (NLP) for the conceptual analysis of results.
Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.
In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.
Designers are entrusted with increasingly complex and impactful challenges. However, the current system of design education does not always prepare students for these challenges. When we examine what and how our system teaches young designers, we discover that the most valuable elements of the designer’s perspective and process are seldom taught. Instead, some designers grow beyond their education through their experience working in industry, essentially learning by accident. Many design programs still maintain an insular perspective and an inefficient mechanism of tacit knowledge transfer.
Meanwhile, skills for developing creative solutions to complex problems are increasingly essential. Organizations are starting to recognize that designers bring something special to this type of work, a rational belief based upon numerous studies that link commercial success to a design-driven approach.
So, what are we to do? Other learned professions such as medicine, law, and business provide excellent advice and guidance embedded within their own histories of professionalization. In this article, we borrow from their experiences to recommend a course of action for design. It will not be easy: it will require a study group to make recommendations for a roster of design and educational practices that schools can use to build a curriculum that matches their goals and abilities. And then it will require a conscious effort to bootstrap the design profession toward both a robust practitioner community and an effective professoriate, capable together of fully realizing the value of design in the 21st century. In this article, we lay out that path.
Artificial Intelligence (AI) plays an increasingly important role in improving HCI and user experience. Yet many challenges persist in designing and innovating valuable human-AI interactions. For example, AI systems can make unpredictable errors, and these errors damage UX and even lead to undesired societal impact. However, HCI routinely grapples with complex technologies and mitigates their unintended consequences. What makes AI different? What makes human-AI interaction appear particularly difficult to design? This paper investigates these questions. We synthesize prior research, our own design and research experience, and our observations when teaching human-AI interaction. We identify two sources of AI's distinctive design challenges: 1) uncertainty surrounding AI's capabilities, 2) AI's output complexity, spanning from simple to adaptive complex. We identify four levels of AI systems. On each level, designers encounter a different subset of the design challenges. We demonstrate how these findings reveal new insights for designers, researchers, and design tool makers in productively addressing the challenges of human-AI interaction going forward.
Information systems (IS) research has always been one of the leading applied research areas in the investigation of technology-related phenomena. Meanwhile, for the past 10 years, artificial intelligence (AI) has transformed every aspect of society more than any other technological innovation. Thus, this is the right time for IS research to foster more quality and high-impact research on AI starting by organizing the cumulated body of knowledge on AI in IS research. We propose a framework called AI capability framework that would provide pertinent and relevant guidance for conducting IS research on AI. Since AI is a fast-evolving phenomenon, this framework is founded on the main AI capabilities that shape today’s fast-moving AI ecosystem. Thus, it is crucial that such a framework engages both AI research and practice into a continuous and evolving dialogue.
Production complexity has increased considerably in recent years due to increasing customer requirements for individual products. At the same time, continuous digitization has led to the recording of extensive, granular production data. Research claims that using production data in data mining methods can lead to managing production complexity effectively. However, manufacturing companies widely do not use such data mining methods. In order to support manufacturing companies in utilizing data mining, this paper presents both a literature review on definitions of data mining, artificial intelligence and machine learning as well as a categorization of existing approaches of applying data mining to manage production complexity.
This paper describes ObjectResponder --- a tool that allows designers to use Artificial Intelligence (AI) to rapidly prototype concepts for context-aware intelligent interaction in the wild. To our knowledge, there are currently no available tools for designing and prototyping with AI within the actual context of use. Our application uses Google Cloud Vision to allow designers assigning chat bot-like responses to objects recognized by the smart-phone camera. This enables designers to use object recognition labels as a means to diverge on possible interpretations of the context and start generating ideas that can then be immediately tested and iterated. Initial results suggest that looking at the world from the perspective of the AI may enable designers to balance human and nonhuman biases, enrich a designer's understanding of the context, and open up unexpected directions for idea generation.
World economies face complex challenges that require new approaches to innovation, collaborating across disciplines and cultures. Climate change, aging population and the need to develop more sustainable ways to produce resources for a growing demand pushed the boundaries of the traditional ways of working. In this context our Academy is tackling the growing need for people that can manage complexity and innovation developing the Design discipline with a multidisciplinary approach.
Artificial intelligence (AI) has recently been highlighted as a design material in the HCI community. This acknowledgement is a call for interaction designers to consider intelligence as a resource for design. While this view is valid and well-grounded, it brings with it a need to better understand how intelligence as a design material can be used in formgiving practices. This chapter seeks to address this need by suggesting a new approach that integrates AI in the designer’s toolkit. This approach considers intelligence as being part of, and expressed through, an object's character, hereby integrating artificial intelligence into a product's form. We describe and discuss this approach by presenting and reflecting on our experiences in a design course where students were asked to give form to intelligent everyday objects in three iterative design cycles. We discuss the implications of our approach and findings within the frame of third wave HCI.
Studies on the development of critical thinking skills with specific curriculum materials and instructional methods are few and have been highly theoretical and far removed from practical concerns and applications. This paper contributes to the existing literature on critical thinking pedagogy by providing examples of written assignments that are designed using the 21st century Bloom’s taxonomy model to contribute to critical thinking development. The step-wise development of critical thinking skills approach is provided. The paper argues that both focus on real problems and issues and provision of clear unambiguous instructions are critical fundamentals of critical thinking skills development. The paper also illustrates how to develop critical thinking assignments by providing five examples of course assignments within business education.
HCI has become particularly interested in using machine learning (ML) to improve user experience (UX). However, some design researchers claim that there is a lack of design innovation in envisioning how ML might improve UX. We investigate this claim by analyzing 2,494 related HCI research publications. Our review confirmed a lack of research integrating UX and ML. To help span this gap, we mined our corpus to generate a topic landscape, mapping out 7 clusters of ML technical capabilities within HCI. Among them we identified 3 under-explored clusters that design researchers can dig in and create sensitizing concepts for. To help operationalize these technical design materials, our analysis then identified value channels through which the technical capabilities can provide value for users: self, context, optimal, and utility-capability. The clusters and the value channels collectively mark starting places for envisioning new ways for ML technology to improve people's lives.
To develop a method enabling human-like, flexible supervisory control via delegation to automation.
Real-time supervisory relationships with automation are rarely as flexible as human task delegation to other humans. Flexibility in human-adaptable automation can provide important benefits, including improved situation awareness, more accurate automation usage, more balanced mental workload, increased user acceptance, and improved overall performance.
We review problems with static and adaptive (as opposed to "adaptable") automation; contrast these approaches with human-human task delegation, which can mitigate many of the problems; and revise the concept of a "level of automation" as a pattern of task-based roles and authorizations. We argue that delegation requires a shared hierarchical task model between supervisor and subordinates, used to delegate tasks at various levels, and offer instruction on performing them. A prototype implementation called Playbook is described.
On the basis of these analyses, we propose methods for supporting human-machine delegation interactions that parallel human-human delegation in important respects. We develop an architecture for machine-based delegation systems based on the metaphor of a sports team's "playbook." Finally, we describe a prototype implementation of this architecture, with an accompanying user interface and usage scenario, for mission planning for uninhabited air vehicles.
Delegation offers a viable method for flexible, multilevel human-automation interaction to enhance system performance while maintaining user workload at a manageable level.
Most applications of adaptive automation (aviation, air traffic control, robotics, process control, etc.) are potential avenues for the adaptable, delegation approach we advocate. We present an extended example for uninhabited air vehicle mission planning.
"Designing With: AI, ML and DV" is an experimental workshop introducing students to the development of collaborative design practices with Artificial Intelligence, Machine Learning and Data Visualization tools.
The workshop will host a multidisciplinary team of lecturers, PhD students and students from the Master of Arts in Interaction Design at SUPSI in Mendrisio, the Master of Architecture at EPFL in Lausanne, the Master of Science in New Media and Web Practice and the Master of Science in Transition, Innovation, and Sustainability Environments at Universidade NOVA in Lisbon and the Master of Science in Space and Communication at HEAD in Geneva.
The workshop is part of the research project 'Designing With. A New Educational Module to Integrate ML, AI, and DV in Design Curricula', funded by the Movetia International Programme and in collaboration with the Institute of Design of SUPSI, the Media x DesignLab of EPFL and the iNOVA Media Lab of the Universidade NOVA de Lisboa.
Designing With: A New Educational Module to Integrate Artificial Intelligence, Machine Learning and Data Visualization in Design Curricula, is an ongoing research project in collaboration between the Institute of Design, SUPSI; the EPFL and the Universidade NOVA de Lisboa.
The aim of the project is to develop an experimental approach and collaborate on the definition of a new educational module suitable to be applied in multidisciplinary environments that integrate Artificial Intelligence (AI), Machine Learning (ML) and Data Visualization (DV) in Design curricula. All the knowledge generated will be documented into an online toolkit containing AI/ML/DV tools, resources and methodologies accessible by an audience of design teachers and students with a training background in different design areas. (designingwithai.ch)
Recent advances in Artificial Intelligence raise interest in its participation in design activity, which is commonly considered to be complex and human-dominated. In this work, we aim to examine AI roles in early design stages. The human ideation components and design tools related to AI are discussed in a framework of AI-augmented design support. The framework develops a hierarchy of design cognition (basis), approaches and principles. The cognitive models are constructed in an empirical study of 30 designers (26 for analysis, 4 for pilot study) by concurrent Think-Aloud protocol and behavior analysis. The process of producing new design ideas is explained by a transparent analysis of designers’ language and behaviors. Three strategies to organize cognitive activities in design ideation are summarized: develop structured consideration, relate to a scenario, and stick-to designing. These strategies suggest AI could act as (1) representation creation, (2) empathy trigger and (3) engagement, in principles of “knowledge-driven” and “decompose-and-integrate”. The design support with AI provides new perspectives on computer-based design tools that limit to well-defined design variables. The framework is built on a generic notion of design activity and “mimic” human design rationales, expected to benefit research of domain-independent computational design supports and cognitive supports.
Machines incorporating techniques from artificial intelligence and machine learning can work with human users on a moment-to-moment, real-time basis to generate creative outcomes, performances and artefacts. We define such systems collaborative, creative AI systems, and in this article, consider the theoretical and practical considerations needed for their design so as to support improvisation, performance and co-creation through real-time, sustained, moment-to-moment interaction. We begin by providing an overview of creative AI systems, examining strengths, opportunities and criticisms in order to draw out the key considerations when designing AI for human creative collaboration. We argue that the artistic goals and creative process should be first and foremost in any design. We then draw from a range of research that looks at human collaboration and teamwork, to examine features that support trust, cooperation, shared awareness and a shared information space. We highlight the importance of understanding the scope and perception of two-way communication between human and machine agents in order to support reflection on conflict, error, evaluation and flow. We conclude with a summary of the range of design challenges for building such systems in provoking, challenging and enhancing human creative activity through their creative agency.
This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education, and machine learning education. It then draws on research about design processes in engineering and creative practice to motivate a set of learning objectives for students who wish to design new creative artifacts with machine learning. The article then draws on education research and knowledge of creative computing practices to propose a set of teaching strategies that can be used to support creative computing students in achieving these objectives. Explanations of these strategies are accompanied by concrete descriptions of how they have been employed to develop new lectures and activities, and to design new experiential learning and scaffolding technologies, for teaching some of the first courses in the world focused on teaching machine learning to creative practitioners. The article subsequently draws on data collected from these courses—an online course as well as undergraduate and masters-level courses taught at a university—to begin to understand how this curriculum supported student learning, to understand learners’ challenges and mistakes, and to inform future teaching and research.
Design Thinking (DT) is spreading out in the managerial community as an alternative way to innovate products and services respect to the classical stage-gate model mostly linked to technology-push innovative patterns. At the same time few disruptive technologies – like Artificial Intelligence (AI) and machine learning – are impacting the ways companies manage their knowledge and activate innovation and design processes. What is the impact that AI is exerting on DT practices? What are the main changes that DT is undergoing? These questions are analyzed in this paper, where the aim consists in increasing the understanding of the transformation that is occurring in DT and more general in innovation practices. Through a qualitative case study analysis made on startups offering AI based solutions supporting multiple or individual DT phases, the article pinpoints few main changes: i) a facilitation in blending the right mix of cultures and creative attitudes in innovation teams; ii) the empowerment of the research phase where statistical significance is gained and user analysis are less observer-biased; iii) the automatization of the prototyping and learning phases.
Machine learning (ML) is now a fairly established technology, and user experience (UX) designers appear regularly to integrate ML services in new apps, devices, and systems. Interestingly, this technology has not experienced a wealth of design innovation that other technologies have, and this might be because it is a new and difficult design material. To better understand why we have witnessed little design innovation, we conducted a survey of current UX practitioners with regards to how new ML services are envisioned and developed in UX practice. Our survey probed on how ML may or may not have been a part of their UX design education, on how they work to create new things with developers, and on the challenges they have faced working with this material. We use the findings from this survey and our review of related literature to present a series of challenges for UX and interaction design research and education. Finally, we discuss areas where new research and new curriculum might help our community unlock the power of design thinking to re-imagine what ML might be and might do.
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