We introduce a new challenge to test the STEM skills of neural models. The problems in the real world often require solutions, combining knowledge from STEM (science, technology, engineering, and math). Unlike existing datasets, our dataset requires the understanding of multimodal vision-language information of STEM. Our dataset features one of the largest and most comprehensive datasets for the challenge. It includes 448 skills and 1,073,146 questions spanning all STEM subjects. Compared to existing datasets that often focus on examining expert-level ability, our dataset includes fundamental skills and questions designed based on the K-12 curriculum. We also add state-of-the-art foundation models such as CLIP and GPT-3.5-Turbo to our benchmark. Results show that the recent model advances only help master a very limited number of lower grade-level skills (2.5% in the third grade) in our dataset. In fact, these models are still well below (averaging 54.7%) the performance of elementary students, not to mention near expert-level performance. To understand and increase the performance on our dataset, we teach the models on a training split of our dataset. Even though we observe improved performance, the model performance remains relatively low compared to average elementary students. To solve STEM problems, we will need novel algorithmic innovations from the community.

Title: Data Science Vs Machine Learning Vs Data Analytics 1 www.simpliv.com 2 What is Data Science? Data Science is a field of technology that deals with exploring, modeling, and analyzing the big data to get meaningful insights from them that can solve a crucial business problem www.simpliv.com Predictive Modeling To predict the outcomes with the help of data models. These models are used for predicting various activities, events, phenomenon, etc. Machine Learning and Deep Learning Machine Learning seeks to educate the machines without human intervention. Deep Learning deals with artificial neural network which is nothing but multiple layers of algorithms.