Data Science Lecture Series: Maximizing Human Potential Using Machine Learning-Driven Applications

Data Science Lecture Series: Maximizing Human Potential Using Machine Learning-Driven Applications
Lecture | September 19 | 1:00-2:30 p.m. | Sutardja Dai Hall, Banatao Auditorium

Speaker/Performer: Vivienne Ming, Chief Scientist at Gild
Sponsors: Berkeley Institute for Data Science, Data, Society and Inference Seminar

The elusive quest to identify and place skilled professionals has become an obsession in the talent wars of the tech industry (not to mention in schools from K though Postdoc). Respected companies such as Google have applied enormous resources to predicting the best developers and managers, and yet they also periodically acknowledge the shortcomings of their existing methodology (e.g., no more brainteasers). We will discuss the concept of continuous passive-implicit assessment, applied to both learners and professionals, from kindergärtners to (future) CEOs. Building cognitive models using unstructured data and ubiquitous sensors allows the assessment not only of concept mastery, but meta-learning development as well (e.g., "Grit" and "Social-Emotional Intelligence"). Such models can then be used to predict which content will be an effective learning experience for a given learner, identify ad hoc cohorts for collaborative learning, and access the value added across educational institutions.

[marklondon]

Interesting. 30 million word gap! I have always felt that there is a correlation between social success factors and linguistic competence. I didn't realise that loading code at 2am would be so valuable, though!!