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Artificial Intelligence and Machine Learning
Published in Yves Caseau, The Lean Approach to Digital Transformation, 2022
The implementation of AI-enhanced applications must be seen systemically as a loop. The virtuous circle in Figure 5.1 is not just about data and algorithms; it is also about the actors in the system—remember the earlier phrase “AI is a learning system with humans in it.” There are two key roles for humans in this diagram. The first is to train the learning system and to develop the algorithms. As we saw in the first part, we do not find the right method right away; engineers gradually learn by trial and error what works and what doesn’t. It is, therefore, important to be able to iterate quickly in order to learn faster. The second human role is that of the user. In the case of a B2C application, it is the customer of the service; in the case of a B2E application, it is the employee who uses the AI-enhanced application to do his job better—the operator in a factory, for example. Taking into account the user’s learning is fundamental to create value with AI. In both roles, human skills must be developed to make the “human + machine” synergy work as well as possible. To summarize, there is a learning curve, and it takes time.19
Communication, Teamwork, Leadership, and Trust
Published in Scott M. Payne, Strategies for Accelerating Cleanup at Toxic Waste Sites, 2020
In order to make informed decisions, people need to learn from the past, which involves communicating successes and failures. Communicating success is common in many agencies and businesses, although not universal. Past successes are often used to promote new approaches, improve methods, or innovative technologies that can be used to complete projects more quickly, save money, or simply illustrate how a difficult job was successfully completed. Communicating successes also brings recognition to the people and teams behind successful efforts, helping to build loyalty and honor people in the workplace. Communicating failures is also necessary because people often learn by their mistakes, which is especially important in cases where trial and error is used in order to remedy a problem. Limiting future trial and error is possible by communicating not only what works, but what does not work, so the same mistakes are avoided.
Importance of Training for Automated, Connected, and Intelligent Vehicle Systems
Published in Donald L. Fisher, William J. Horrey, John D. Lee, Michael A. Regan, Handbook of Human Factors for Automated, Connected, and Intelligent Vehicles, 2020
Alexandria M. Noble, Sheila Garness Klauer, Sahar Ghanipoor Machiani, Michael P. Manser
Generally, experience can improve performance, but learning through trial and error can waste time, may lead to a less-than-ideal solution, or may never result in a solution at all. Trial and error can also result in negative effects, such as increased frustration, reduced learner motivation, discontinued use of the system, or mental model recalibration through potentially dangerous experiences. Learning by doing is not a homogenous process. A study by Pereira, Beggiato, and Petzoldt (2015) found that mastering the use of ACC took different lengths of time. Furthermore, Larsson (2012) conducted a survey of 130 ACC users. The results indicated that drivers need to be especially attentive in those situations to which, during conventional driving, they would not be attentive. The system may not be self-explanatory enough for a strictly trial-and-error based approach. Larsson’s survey results indicated that as drivers gained experience using the ACC system, they became more aware of the system’s limitations. However, other studies have shown that safety-critical misunderstandings of system limitations are resilient and can persist over time (Kyriakidis et al., 2015; Llaneras, 2006). Tversky & Kahneman (1971) argued that users tended to place undue confidence in the stability of observed patterns, thus resulting in misunderstandings that are not corrected when relying solely on trial-and-error learning methods.
Innovation in a box: exploring creativity in design for additive manufacturing in a regulated industry
Published in Journal of Engineering Design, 2022
Angelica Lindwall, Christo Dordlofva, Anna Öhrwall Rönnbäck, Peter Törlind
For an engineer to expand their expertise level and hence their creative abilities, they need to create knowledge in their design practices. A person goes through five steps to create skills and knowledge (Cheetham and Chivers 2005): Novice (step 1); Advanced beginner (step 2); Competent (step 3); Proficient (step 4); and Expert (step 5). Novice learners often follow the rules and guidelines, while experts do not rely on such supports (ibid.). However, expert engineers tend to use various design strategies, that novice engineers do not have the same knowledge or understanding of yet (Ahmed, Wallace, and Blessing 2003). Design teams are often diverse, with various knowledge base levels amongst individuals in various areas. There is a need for engineers to exchange knowledge with each other, support each other in developing new skills, and increase the total knowledge within the design team (Mamykina, Candy, and Edmonds 2002). As novice learners in a certain area, engineers often need to approach design problems using a ‘trial and error’ approach (Ahmed, Wallace, and Blessing 2003). Therefore, it is important to have an open environment where engineers feel that they can make mistakes while learning (Dostaler 2010; Mamykina, Candy, and Edmonds 2002). Additionally, it is suggested that engineers going through the first steps of creating knowledge need to have engaged design coaches to fully understand the new aspects of design (Dym et al. 2005).
Performance improvement of mode division multiplexing free space optical communication system through various atmospheric conditions with a decision feedback equalizer
Published in Cogent Engineering, 2022
Abdullah Almogahed, Angela Amphawan, Fathey Mohammed, Abdulwadood Alawadhi
Obtaining the best DFE performance depends on how many FFF and FBF taps were used to reclaim the source signal. The possible number of FFF taps in OptSim 5.2 is between 1 and 100, while the number of FBF taps that can be used is between 0 and 100. Choosing the appropriate number of FFF and FBF taps to get the best performance in our experiments relies on the trial-and-error method. Trial and error are defined as a series of repeated, varied attempts that are carried out until success is achieved.
A comparative review of zero-waste fashion design thinking and operational research on cutting and packing optimisation
Published in International Journal of Fashion Design, Technology and Education, 2022
Nesma ElShishtawy, Pammi Sinha, Julia A. Bennell
One of the main limitations facing operational research is ignoring the flexibility in the problem constraints. This pragmatism would require human intervention to adjust the shapes and orientation. Such that, in solving the marker planning problem, different solution methods and techniques are primarily based on trial and error. Techniques that provide good solutions can be adopted and expanded using more trial and error to find better solutions.