Common Pitfalls and Recommendations for Grand Challenges in Medical Artificial Intelligence

2021 ◽  
Author(s):  
Annika Reinke ◽  
Minu D. Tizabi ◽  
Matthias Eisenmann ◽  
Lena Maier-Hein
Keyword(s):  
Artificial Intelligence ◽  
Grand Challenges

Tackling the Grand Challenge of Algorithmic Opacity Through Principled Robust Action

2021 ◽  
Vol 1 (1) ◽  
pp. 76-87
Author(s):  
Alexander Buhmann ◽  
Christian Fieseler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recent Work ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Grand Challenge ◽  
Negative Effects ◽  
Grand Challenges ◽  
Fluid Design

Organizations increasingly delegate agency to artificial intelligence. However, such systems can yield unintended negative effects as they may produce biases against users or reinforce social injustices. What pronounces them as a unique grand challenge, however, are not their potentially problematic outcomes but their fluid design. Machine learning algorithms are continuously evolving; as a result, their functioning frequently remains opaque to humans. In this article, we apply recent work on tackling grand challenges though robust action to assess the potential and obstacles of managing the challenge of algorithmic opacity. We stress that although this approach is fruitful, it can be gainfully complemented by a discussion regarding the accountability and legitimacy of solutions. In our discussion, we extend the robust action approach by linking it to a set of principles that can serve to evaluate organisational approaches of tackling grand challenges with respect to their ability to foster accountable outcomes under the intricate conditions of algorithmic opacity.

scholarly journals A Focus on Soft Actuation

Actuators ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 74 ◽  
Author(s):  
Miriyev
Keyword(s):  
Artificial Intelligence ◽  
Functional Materials ◽  
Soft Material ◽  
Soft Robotics ◽  
Functional Material ◽  
Research Focus ◽  
Main Research ◽  
Soft Actuator ◽  
Grand Challenges ◽  
Actuator Design

The present editorial paper analyzes the hundred recent research works on soft actuation to understand the current main research focus in the light of the grand challenges in the field. Two characteristic paper types were obtained: one focuses on soft actuator design, manufacturing and demonstration, while another includes in addition the development of functional materials. Although vast majority of the works showcased soft actuation, evaluation of its robustness by multi-cyclic actuation was reported in less than 50% of the works, while only 10% described successful actuation for more than 1000 cycles. It is suggested that broadening the research focus to include investigation of mechanisms underlying the degradation of soft functional material performance in real cyclic actuation conditions, along with application of artificial intelligence methods for prediction of muscle behavior, may allow overcoming the reliability issues and developing robust soft-material actuators. The outcomes of the present work might be applicable to the entire soft robotics domain.

General AI

2020 ◽  
pp. 194-206
Author(s):  
Stephen K. Reed
Keyword(s):  
Artificial Intelligence ◽  
Standard Model ◽  
Nobel Prize ◽  
Cognitive Skills ◽  
General Purpose ◽  
Intelligence Tests ◽  
Grand Challenges ◽  
Wide Range ◽  
The Mind

People use their cognitive skills to solve a wide range of problems whereas computers solve only a limited number of specific problems. A goal of artificial intelligence (AI) is to build on its previous success in specific environments to advance toward the generality of human level intelligence. People are efficient general-purpose learners who can adapt to many situations such as navigating in spatial environments and communicating by using language. To compare human and machine reasoning the AI community has proposed a standard model of the mind. Measuring progress in achieving general AI will require a wide variety of intelligence tests. Grand challenges, such as helping scientists win a Nobel prize, should stimulate development efforts.

Tackling the Grand Challenge of Algorithmic Opacity Through Principled Robust Action

2021 ◽  
Vol 1 (1) ◽  
pp. 76-87
Author(s):  
Alexander Buhmann ◽  
Christian Fieseler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recent Work ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Grand Challenge ◽  
Negative Effects ◽  
Grand Challenges ◽  
Fluid Design

Organizations increasingly delegate agency to artificial intelligence. However, such systems can yield unintended negative effects as they may produce biases against users or reinforce social injustices. What pronounces them as a unique grand challenge, however, are not their potentially problematic outcomes but their fluid design. Machine learning algorithms are continuously evolving; as a result, their functioning frequently remains opaque to humans. In this article, we apply recent work on tackling grand challenges though robust action to assess the potential and obstacles of managing the challenge of algorithmic opacity. We stress that although this approach is fruitful, it can be gainfully complemented by a discussion regarding the accountability and legitimacy of solutions. In our discussion, we extend the robust action approach by linking it to a set of principles that can serve to evaluate organisational approaches of tackling grand challenges with respect to their ability to foster accountable outcomes under the intricate conditions of algorithmic opacity.

The Effect of Empathic Technology on Artificial Intelligence

2014 ◽  
Vol 596 ◽  
pp. 183-187
Author(s):  
Ji Guang Liu ◽  
Qing Li
Keyword(s):  
Artificial Intelligence ◽  
Congestion Control ◽  
Grand Challenges ◽  
Recent Advances

Recent advances in homogeneous archetypes and interposable methodologies are based entirely on the assumption that congestion control and spreadsheets are not in conflict with super pages. Even though it is never a natural objective, it fell in line with our expectations. In fact, few futurists would disagree with the synthesis of scatter/gather I/O. Maranatha, our new algorithm for pseudorandom technology, is the solution to all of these grand challenges.

scholarly journals Tackling the Grand Challenge of Algorithmic Opacity Through Principled Robust Action

2021 ◽  
Vol 1 (1) ◽  
pp. 74-85
Author(s):  
Alexander Buhmann ◽  
Christian Fieseler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recent Work ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Grand Challenge ◽  
Negative Effects ◽  
Grand Challenges ◽  
Fluid Design

Organizations increasingly delegate agency to artificial intelligence. However, such systems can yield unintended negative effects as they may produce biases against users or reinforce social injustices. What pronounces them as a unique grand challenge, however, are not their potentially problematic outcomes but their fluid design. Machine learning algorithms are continuously evolving; as a result, their functioning frequently remains opaque to humans. In this article, we apply recent work on tackling grand challenges though robust action to assess the potential and obstacles of managing the challenge of algorithmic opacity. We stress that although this approach is fruitful, it can be gainfully complemented by a discussion regarding the accountability and legitimacy of solutions. In our discussion, we extend the robust action approach by linking it to a set of principles that can serve to evaluate organisational approaches of tackling grand challenges with respect to their ability to foster accountable outcomes under the intricate conditions of algorithmic opacity.

scholarly journals Special Issue: Artificial Intelligence and Engineering Design

2021 ◽  
pp. 1-6
Author(s):  
James T. Allison ◽  
Michel-Alexandre Cardin ◽  
Christopher McComb ◽  
Yi Ren ◽  
Daniel Selva ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Engineering Design ◽  
Design Knowledge ◽  
Special Issue ◽  
Design Processes ◽  
Grand Challenges ◽  
Research Activities ◽  
New Strategies

Abstract Artificial Intelligence (AI) has had a strong presence in engineering design for decades, and while theory, methods, and tools for engineering design have advanced significantly during this time, many grand challenges remain. Modern advancements in AI, including new strategies for capturing, storing, and analyzing data, have the potential to revolutionize engineering design processes in a variety of ways. The purpose of this special issue is to consolidate recent research activities that utilize existing or new AI methods to advance engineering design knowledge and capabilities.

scholarly journals Organizational Learning

2021 ◽  
pp. 467-484
Author(s):  
Mary Crossan ◽  
Dusya Vera ◽  
Seemantini Pathak
Keyword(s):  
Artificial Intelligence ◽  
Organizational Learning ◽  
Strategic Management ◽  
Strategy Research ◽  
Strategic Renewal ◽  
Grand Challenges ◽  
Deep Reservoir ◽  
Exploration Exploitation

Strategy scholars and practitioners can tap into the deep reservoir of organizational learning (OL) research to address: how is OL uniquely positioned to contribute to the advancement of strategic management? Elevating OL in strategy research has never been more important, given the grand challenges facing our world, requiring strategy to extend beyond organizations to society. This chapter addresses the underutilization of OL in strategy and offers two important areas to leverage: (1) exploration, exploitation, and ambidexterity; and (2) the processes of OL as they relate to strategic renewal and agility, including multilevel theorizing. The chapter also underscores the importance of OL for emerging frontiers at the interface between the human and nonhuman aspects of OL and strategy, including artificial intelligence. The context of strategy has been consistently described as being dynamic, ambiguous, uncertain, and complex, elevating the role of the agent and the quality of judgment and wisdom required within the domain of strategy.

scholarly journals Is it time for artificial intelligence to predict the function of natural products based on 2D-structure

MedChemComm ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 1667-1677 ◽  
Author(s):  
Miaomiao Liu ◽  
Peter Karuso ◽  
Yunjiang Feng ◽  
Esther Kellenberger ◽  
Fei Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Products ◽  
Grand Challenges ◽  
2D Structure

One of chemistry's grand challenges is to find a function for every known metabolite. We explore the opportunity for artificial intelligence to provide rationale interrogation of metabolites to predict their function.

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