The urgency of finding solutions to global energy, sustainability, and healthcare challenges has motivated rethinking of the conventional chemistry and material science workflows. Self‐driving labs, emerged through integration of disruptive physical and digital technologies, including robotics, additive manufacturing, reaction miniaturization, and artificial intelligence, have the potential to accelerate the pace of materials and molecular discovery by 10–100X. Using autonomous robotic experimentation workflows, self‐driving labs enable access to a larger part of the chemical universe and reduce the time‐to‐solution through an iterative hypothesis formulation, intelligent experiment selection, and automated testing. By providing a data‐centric abstraction to the accelerated discovery cycle, in this perspective article, the required hardware and software technological infrastructure to unlock the true potential of self‐driving labs is discussed. In particular, process intensification as an accelerator mechanism for reaction modules of self‐driving labs and digitalization strategies to further accelerate the discovery cycle in chemical and materials sciences are discussed.
This content will become publicly available on July 1, 2024
Research examining the rise of digital environmental governance, particularly at the subnational scale in China, is fairly limited. Critical questions regarding how digital technologies applied at the subnational level may shape or transform environmental governance are only beginning to be explored, given cities’ increasing role as sustainability experimenters and innovators. In this study, we investigate how smart city initiatives that incorporate big data, artificial intelligence, 5G, Internet of Things, and information communication technologies, may play a role in the transformation towards a “digital China.” We conceptualize three major pathways by which digital technology could transform environmental governance in China: through the generation of new data to address existing environmental data gaps; by enhancing the policy analytical capacity of environmental actors through the use of automation, digitalization, and machine learning/artificial intelligence; and last, through reshaping subnational-national, and state-society interactions that may shift balances of power. With its dual prioritization of digital technologies and climate change, China presents an opportunity for examining digitalization trends and to identify gaps in governance and implementation challenges that could present obstacles to realizing the transformative potential of digital environmental management approaches.
more » « less- Award ID(s):
- 1932220
- NSF-PAR ID:
- 10427962
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Environment and Planning B: Urban Analytics and City Science
- Volume:
- 51
- Issue:
- 3
- ISSN:
- 2399-8083
- Format(s):
- Medium: X Size: p. 572-589
- Size(s):
- ["p. 572-589"]
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
-
more » « less
Great claims have been made about the benefits of dematerialization in a digital service economy. However, digitalization has historically increased environmental impacts at local and planetary scales, affecting labor markets, resource use, governance, and power relationships. Here we study the past, present, and future of digitalization through the lens of three interdependent elements of the Anthropocene: ( a) planetary boundaries and stability, ( b) equity within and between countries, and ( c) human agency and governance, mediated via ( i) increasing resource efficiency, ( ii) accelerating consumption and scale effects, ( iii) expanding political and economic control, and ( iv) deteriorating social cohesion. While direct environmental impacts matter, the indirect and systemic effects of digitalization are more profoundly reshaping the relationship between humans, technosphere and planet. We develop three scenarios: planetary instability, green but inhumane, and deliberate for the good. We conclude with identifying leverage points that shift human–digital–Earth interactions toward sustainability.
-
Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart and sentient. We examine the new vantages that smart and sentient retail high streets provide on the customer journey, and how they could transform retailers’ sway over customer experience with new reach to the public spaces around shops. In doing so, we pursue a three-way consideration of these issues, examining the technology that underpins smart retailing, new advances in artificial intelligence and machine learning that beget a level of street-side sentience, and opportunities for retailers to map the knowledge that those technologies provide to individual customer journeys in outdoor settings. Our exploration of these issues takes form as a review of the literature and the introduction of our own research to prototype smart and sentient retail systems for high streets. The topic of enhancing retailers’ acuity on high streets has significant currency, as many high street stores have recently been struggling to sustain custom. However, the production and application of smart and sentient technologies at hyper-local resolution of the streetscape conjures some sobering considerations about shoppers’ and pedestrians’ rights to privacy in public.more » « less
-
more » « less
Abstract To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making.
Practitioner notes What is already known about this topic
Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.
While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.
There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.
What this paper adds
Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.
Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.
Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.
Implications for practice and/or policy
It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.
Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.
To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).
-
more » « less
Abstract Computational systems, including machine learning, artificial intelligence, and big data analytics, are not only inescapable parts of social life but are also reshaping the contours of law and legal practice. We propose turning more law and social science (LSS) attention to new technological developments through the study of “law in computation,” that is, computational systems' integration with regulatory and administrative procedures, the sociotechnical infrastructures that support them, and their impact on how individuals and populations are interpellated through the law. We present a range of cases in three areas of inquiry ‐ algorithmic governance, jurisdiction and agency ‐ on issues such as immigration enforcement, data sovereignty, algorithmic warfare, biometric identity regimes, and gig economies, for which examining law in computation illuminates how new technological systems' integration with legal processes pushes the distinction between “law on the books” and “law in action” into new domains.We then propose future directions and methods for research. As computational systems become ever more sophisticated, understanding the law in computation is critical not only for LSS scholarship, but also for everyday civics.
