Whether you’re a computer science student, an entry-level data scientist or a technology professional wanting to learn more, All Things Data is for you. Our members can immediately take what they learned from our workshops and apply it in the workplace.
Keywords: machine learning, neural networks, TensorFlow, CNTK, Torch, CNN, RNN, optimization, logistic regression, data visualization, Honeypots, voting, relevance evaluation, information extraction, human debugging, HIT debugging, quality framework, deep learning, Google CoLab, Azure cloud, text classification
Many data science applications that use machine learning techniques depend on humans providing the initial data set so algorithms can process the rest or to evaluate the performance of such algorithms. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see the emergence of novel infrastructure that combines computations performed by humans and machines. Building these labeling pipelines remain difficult and these difficulties need to be addressed by practitioners and researchers to advance the state of the art. In this talk, I’ll outline challenges and opportunities when designing and implementing computation systems that use humans and machines.
About our speaker:
Omar Alonso is a Principal Applied Scientist with Microsoft where he works on the intersection of information retrieval, social data, human computation, and knowledge graph generation. He is the co-chair of the Human Computation and Crowdsourcing track at WWW'19 and on the organizing committee for HCOMP'19. His recent book, "The Practice of Crowdsourcing", provides a practical view for implementing large scale labeling. He is an instructor at Northeastern University - San Francisco Bay Area and holds a Ph.D. in CS from UC Davis.
Our devices and its cloud-based APIs use Artificial Intelligence (AI) to choose for us a set of candidate actions. As users, our routine is influenced by algorithmic recommendations to watch a movie, add someone on a social network, or read the news. If this technology is so useful, then what is the problem with its use at a large scale? Recently, these algorithms have been blamed for encoding bias and stereotypes unveiling potential ethical issues, including that they may be less precise for individuals from under-represented groups. Florez will talk about the Math responsible for discriminative power and why it is important that Women, LatinX, Afro-Americans, and LGTB groups get involved in promoting the development of AI technology.
About our speaker:
Omar Florez is a Senior Research Manager at Capital One working on enabling natural conversations between humans and devices. He was also a Machine Learning Researcher at Intel Research Labs focused on teaching computers to understand user context by discovering patterns in images (visual question answering) and audio (prediction of acoustic events) and is a recipient of the IBM Innovation Award on Large-Scale Analytics. He used deep learning for accurate predictions and Bayesian models for discovering interpretable hypotheses. He holds a Ph.D. in CS from Utah State University.