Many times AI has been put on a pedestal as the future of x y & z, however, many seem to agree that education is a sector in particular which will see stark changes in both admin, teaching styles, personalisation and more. I had the pleasure of speaking to three individuals working in the field, including, Vinod Bakthavachalam, Senior Data Scientist at Coursera, Kian Katanforoosh, Lecturer at Stanford University & Sergey Karayev, Co-Founder and CTO of Gradescope. We began by having Sergey of Gradescope walk us through his product, which has been recently acquired by turnitin. The concept, it seemed was formed from the simple and widespread issue of both lack of consistency, lack of insight through time constraint and delayed feedback on academic work. Sergey found that scanning the papers onto an online interface when paired with a rubric can allow for accurate marking in seconds across several papers.
Instructors returning to high school and college classes this fall, take note: Grading your students' work is about to get a lot easier. A UC Berkeley professor and three former graduate students are putting the finishing touches on an artificial intelligence technology that groups answers and allows them to be graded en masse. The AI-boosted capability, now wrapping up beta testing before becoming available this fall, will be the newest feature of the online grading application Gradescope. The team launched the app as a company two years ago, in part to stem cheating. Having a digital record of a graded paper makes it hard to alter written answers and argue the paper was incorrectly graded.
Four UC Berkeley researchers developed a program to help grade papers during their time working as teaching assistants – and now, they've added artificial intelligence to their app to help instructors speed up the grading process. The team launched the online grading app Gradescope two years ago and have accumulated 10 million answers to around 100,000 questions from a wide range of college courses – the app has already shortened the grading process by 50 percent due to its friendly interface and the ability for multiple teaching assistants to grade papers in parallel. Their new AI features addresses three challenges: identify question types, distinguishing between different written marks, and recognizing handwriting. AI helps turn grading into an automated, highly repeatable exercise by learning to identify and group answers, and thus treat them as batches. The addition of AI promises to slash grading times by as much as 90 percent, said Sergey Karayev, a Gradescope co-founder who finished his PhD in computer science in 2014.
There are swathes of blogs covering the impact of AI on both the financial and insurance industries, however, many look at farfetched AI and ML concepts, not yet tested or applied in either. The below list of'uses' documents application methods or techniques which are currently being implemented, albeit quietly, slowly and behind the scenes. The below are six ways in which we think AI is best being utilised in both the finance and insurance industries. Considered one of the more sought after applications of AI in Finance, it is suggested that the use of AI for fraud detection could detect billions of dollars worth of fraudulent transactions. Whilst AI is already somewhat prevalent in the financial industry, it is expected that by the end of 2021, the amount spent on applying AI in finance with specific focus on fraud detection is set to triple.
Forbus, Kenneth D. (Northwestern University) | Garnier, Bridget (University of Wisconsin-Madison) | Tikoff, Basil (University of Wisconsin-Madison) | Marko, Wayne (Northwestern University) | Usher, Madeline (Northwestern University) | McLure, Matthew (Northwestern University)
Sketching can be a valuable tool for science education, but it is currently underutilized. Sketch worksheets were developed to help change this, by using AI technology to give students immediate feedback and to give instructors assistance in grading. Sketch worksheets use visual representations automatically computed by CogSketch, which are combined with conceptual information from the OpenCyc ontology. Feedback is provided to students by comparing an instructor’s sketch to a student’s sketch, using the Structure-Mapping Engine. This paper describes our experiences in deploying sketch worksheets in two types of classes: Geoscience and AI. Sketch worksheets for introductory geoscience classes were developed by geoscientists at University of Wisconsin-Madison, authored using CogSketch and used in classes at both Wisconsin and Northwestern University. Sketch worksheets were also developed and deployed for a knowledge representation and reasoning course at Northwestern. Our experience indicates that sketch worksheets can provide helpful on-the-spot feedback to students, and significantly improve grading efficiency, to the point where sketching assignments can be more practical to use broadly in STEM education.