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Rosalind Picard is a professor at MIT, director of the Affective Computing Research Group at the MIT Media Lab, and co-founder of two companies, Affectiva and Empatica. Over two decades ago she launched the field of affective computing with her book of the same name. This book described the importance of emotion in artificial and natural intelligence, the vital role emotion communication has to relationships between people in general and in human-robot interaction. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you

Rosalind Picard is a professor at MIT, director of the Affective Computing Research Group at the MIT Media Lab, and co-founder of two companies, Affectiva and Empatica. Over two decades ago she launched the field of affective computing with her book of the same name. This book described the importance of emotion in artificial and natural intelligence, the vital role emotion communication has to relationships between people in general and in human-robot interaction. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you

Emotions have an important role in education. Affective development, attitudes, and emotions in learning are measured using affective assessment. This method is the right way to determine the student’s affective development. However, the process did not run optimally because the teacher found it difficult to collect student’s affective data. This paper describes the development of a system that can assist teachers in carrying out affective assessment. The system was developed using a v-model that aligns the verification phase with the validation. The use of the system is carried out during learning activities. The emotion detection system detects through body gestures using PoseNet to generate emotional data for each student. The detection results are then processed and displayed on an information system in the form of a website for affective assessment. The accuracy of emotion detection got validation values of 84.4% and 80.95% after being tested at school. In addition, the acceptance test with the usability aspect of the system by the teacher got a score of 77.56% and a score of 79.85% by the students. Based on several tests carried out, this developed system can assist the process of implementing affective assessment.

New Topics in Social Computing was a lecture series organized by Joanne McNeil at Eyebeam. This event, "Affective Computing" included Sabrina Majeed, Sarah Jaffe, and Lauren McCarthy   Held on Oct 29, 2014 https://web.archive.org/web/20141008222408/https://eyebeam.org/events/new-topics-in-social-computing-emotional-labor-and-affective-computing For more on the series:  https://medium.com/message/how-i-organized-a-series-of-six-panels-on-technology-with-only-women-speaking-bcb40a94f033

New Topics in Social Computing was a lecture series organized by Joanne McNeil at Eyebeam. This event, "Affective Computing" included Sabrina Majeed, Sarah Jaffe, and Lauren McCarthy   Held on Oct 29, 2014 https://web.archive.org/web/20141008222408/https://eyebeam.org/events/new-topics-in-social-computing-emotional-labor-and-affective-computing For more on the series:  https://medium.com/message/how-i-organized-a-series-of-six-panels-on-technology-with-only-women-speaking-bcb40a94f033

Depression is a psychiatric disorder with high morbidity, high clinical cure but low treatment acceptance, and high relapse rates. It is mainly characterized by significant and persistent depressed mood, and some patients may have self-injurious and suicidal behaviors, and may even be accompanied by psychotic symptoms such as delusions and hallucinations. There are various means of detecting depression, but there is often misdiagnosis and underdiagnosis. In order to better recognize whether an individual is suffering from depression, can we detect it through some more precise means? For example, can we determine whether an individual is suffering from depression through indicators such as facial expression, behavioral gestures, voice tone, etc.?

This is a protocol for a scoping review on the use of affective computing in video conference between clinician and patient. The purpose of the scoping review is to map and analyze the published literature on the subject.

New electronic technologies provide powerful tools for managing and processing the rapidly increasing amounts of information available for learning; teachers, however, have often been slow in integrating computers into the curriculum. This study addresses the question of how prospective teachers construct affective and cognitive models about computer environments. The study involved 177 first year students taking two different courses at a rural university in Australia; the first group was composed of 92 pre-service elementary school teacher trainees who were involved in a technology subject containing a computing component and the second group was made up of students majoring in computer studies. Students in both groups were administered a mixture of instruments that included affective (anxiety and attitude) and cognitive (knowledge and mental models) measures through questionnaires and interviews. Teacher education students as a group were found to be less knowledgeable, more anxious, less confident, and to have less liking for interactions with computers than the other group. Among teacher education students, female students were found to be more anxious and less confident with computers than male students; there were no anxiety differences between male and female students in the computer science course. Carefully planned courses and application experience addressing both affective responses and cognitive understandings are needed in order for teacher education students to be able to use technology effectively in classroom teaching and learning. (Contains 30 references.) (AEF)

New electronic technologies provide powerful tools for managing and processing the rapidly increasing amounts of information available for learning; teachers, however, have often been slow in integrating computers into the curriculum. This study addresses the question of how prospective teachers construct affective and cognitive models about computer environments. The study involved 177 first year students taking two different courses at a rural university in Australia; the first group was composed of 92 pre-service elementary school teacher trainees who were involved in a technology subject containing a computing component and the second group was made up of students majoring in computer studies. Students in both groups were administered a mixture of instruments that included affective (anxiety and attitude) and cognitive (knowledge and mental models) measures through questionnaires and interviews. Teacher education students as a group were found to be less knowledgeable, more anxious, less confident, and to have less liking for interactions with computers than the other group. Among teacher education students, female students were found to be more anxious and less confident with computers than male students; there were no anxiety differences between male and female students in the computer science course. Carefully planned courses and application experience addressing both affective responses and cognitive understandings are needed in order for teacher education students to be able to use technology effectively in classroom teaching and learning. (Contains 30 references.) (AEF)