Making Sense of AI Systems Development

e rise of generative AI has led many companies to hire freelancers to harness its potential. However, this technology presents unique challenges to developers who have not previously engaged with it. Freelancers may find these challenges daunting due to the absence of organizational support and their reliance on positive client feedback. In a study involving 52 freelance developers, we identified multiple challenges associated with developing solutions based on generative AI. Freelancers oen struggle with aspects they perceive as unique to generative AI such as unpredictability of its output, the occurrence of hallucinations, and the inconsistent effort required due to trial-and-error prompting cycles. Further, the limitations of specific frameworks, such as token limits and long response times, add to the complexity. Hype-related issues, such as inflated client expectations and a rapidly evolving technological ecosystem, further exacerbate the difficulties. To address these issues, we propose Soware Engineering for Generative AI (SE4GenAI) and Hype-Induced Soware Engineering (HypeSE) as areas where the soware engineering community can provide effective guidance. is support is essential for freelancers working with generative AI and other emerging technologies. CCS CONCEPTS• Soware and its engineering → Soware creation and management; • Computing methodologies → Artificial Intelligence.

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A New Approach to the Quality Determination of Used Palm Cooking Oil using Supervised Learning based on Electronic Sensors

Anifah,

Wikandari,

Rusimamto

et al. 2024

Eng. Technol. Appl. Sci. Res.

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As discarding used palm oil in nature is very dangerous, a processing mechanism is needed to utilize it according to needs. This utilization depends on the palm oil used, so the sorting process becomes important. This study proposes a new classification approach for the quality of used palm oil using Self-Organizing Map (SOM), Linear Vector Quantization (LVQ), and K-means, based on electronic sensors. This study included hardware design, software development, data collection, and training and testing processes. Based on the experimental results, the proposed system performed well using 13 parameters consisting of e-nose data, color, viscosity, and turbidity. The accuracy of SOM was 91.11%, LVQ achieved 95.56%, and K-Means obtained an accuracy of 98.89%. This system can be used as a decision support system in the automatic recognition of used palm oil to classify its quality.

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Making Sense of AI Systems Development

Cited by 4 publications

References 77 publications

Artificial intelligence in open innovation project management: A systematic literature review on technologies, applications, and integration requirements

Artificial intelligence in open innovation project management: A systematic literature review on technologies, applications, and integration requirements

Development in times of hype: How freelancers explore Generative AI?

A New Approach to the Quality Determination of Used Palm Cooking Oil using Supervised Learning based on Electronic Sensors

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