The Multimodal And Modular Ai Chef: Complex Recipe Generation From Imagery

The AI community has embraced multi-sensory or multi-modal approaches to advance this generation of AI models to resemble expected intelligent understanding. Combining language and imagery represents a familiar method for specific tasks like image captioning or generation from descriptions. This paper compares these monolithic approaches to a lightweight and specialized method based on employing image models to label objects, then serially submitting this resulting object list to a large language model (LLM). This use of multiple Application Programming Interfaces (APIs) enables better than 95% mean average precision for correct object lists, which serve as input to the latest Open AI text generator (GPT-4). To demonstrate the API as a modular alternative, we solve the problem of a user taking a picture of ingredients available in a refrigerator, then generating novel recipe cards tailored to complex constraints on cost, preparation time, dietary restrictions, portion sizes, and multiple meal plans. The research concludes that monolithic multimodal models currently lack the coherent memory to maintain context and format for this task and that until recently, the language models like GPT-2/3 struggled to format similar problems without degenerating into repetitive or non-sensical combinations of ingredients. For the first time, an AI chef or cook seems not only possible but offers some enhanced capabilities to augment human recipe libraries in pragmatic ways. The work generates a 100-page recipe book featuring the thirty top ingredients using over 2000 refrigerator images as initializing lists

A Case Study on the Efficacy of STEM Pedagogy in Central New York State: Examining STEM Engagement Gaps Affecting Outcomes for High School Seniors and Post-2007 Educational Leadership Interventions to Reinforce STEM Persistence with Implications of STEM Theoretic Frameworks on Artificial Intelligence / Machine Learning

STEM (science, technology, engineering, and mathematics) has gained significant notoriety and momentum in recent years. STEM literacy highlights the vital connection between an educated STEM workforce and U.S. national prosperity and leadership. STEM educational and job placement goals have been a national priority for over the past 20 years. However, the STEM gap is widening—contributing to increasing STEM pipeline leakage and the social injustice milieu of a noncompetitive workforce— undermining efforts to create prosperity and sustain global leadership. The pace of STEM jobs filled lags the rate of technological advancement and the surges in skilled STEM labor demand. The aggregate disparity over time has troubling implications. The purpose of the study was to examine the STEM gap touchpoints for a Central New York high school during the transition period upon entering college or the workforce. A qualitative case study used Lesh’s translation model as a research framework. A semi-structured, focus group protocol was employed to gain a fresh perspective on the STEM gap problem and identify purposeful interventions. A major finding was the slow pace of adopting institutional reforms that replaces standardscompetency-based learning with progressive application- and outcome-based pedagogy. The study has implications for school districts, secondary schools, and higher education teacher preparedness programs in STEM pedagogy and curriculum development. A knowledge-based, progressive STEM theoretic framework with pedagogical scaffolding is conceptualized rooted in artificial intelligence and machine learning. The study presents recommendations for school districts, secondary education teachers, state education and legislative leaders, higher education institutions, and future research