AI News, Machine Learning for Large Documents, Social Science and Language-Based Games

Machine Learning for Large Documents, Social Science and Language-Based Games

Opening up the Black Box: Interactive Machine Learning for Understanding Large Document Collections, Characterizing Social Science, and Language-Based Games - Abstract Machine learning is ubiquitous, but most users treat it as a black box: a handy tool that suggests purchases, flags spam, or autocompletes text.

I present qualities that ubiquitous machine learning should have to allow for a future filled with fruitful, natural interactions with humans: interpretability, interactivity, and an understanding of human qualities.

I begin with a traditional information processing task---making sense and categorizing large document collections---and show that machine learning methods can provide interpretable, efficient techniques to categorize large document collections with a human in the loop.

Jordan's research focus is in applying machine learning and Bayesian probabilistic models to problems that help us better understand social interaction or the human cognitive process.

Jordan's research focus is in applying machine learning and Bayesian probabilistic models to problems that help us better understand social interaction or the human cognitive process.

G-Research Lecture with Professor Michael I. Jordan on Artificial Intelligence

The G-Research Lecture Series returns with this specially commissioned lecture with Professor Michael I. Jordan titled 'Artificial Intelligence: Perspectives and ...

Not What but Why: Machine Learning for Understanding Genomics | Barbara Engelhardt | TEDxBoston

Machine learning and artificial intelligence are changing the nature of biological research, especially genomics. Artificial intelligence applications are opening ...

IST Lecture: "On computational thinking, inferential thinking and data science" by Michael I. Jordan

On November 8, 2017, Michael I. Jordan, one of the world's most influential computer scientists working on machine learning, statistics and artificial intelligence ...

When will the artificial intelligence replace the human | Jiří Materna | TEDxTrencin

Jiří Materna spoke about his field of business - artificial neural network programming and introduced his project of machine learning with regard to art ...

Perspectives and Challenges, SysML 2018 Invited Talk | Michael I. Jordan, UC Berkeley

The Rise of the AI: Impact of AI and Machine Learning in Construction

The field of construction is well placed to benefit from the advent of machine learning and artificial intelligence. As part of the BIM 360 Project IQ Team at ...

How Machine Intelligence Can Improve Health Care - Prof. Suchi Saria

Recorded May 1st, 2018 ICLR2018 Augmenting Clinical Intelligence with Machine Intelligence "Healthcare is rapidly becoming a data-intensive discipline, ...

"Advances in Deep Neural Networks," at ACM Turing 50 Celebration

Deep neural networks can be trained with relatively modest amounts of information and then successfully be applied to large quantities of unstructured data.

Pedro Domingos: the Five Tribes of Machine Learning

Pedro Domingos is Professor, Machine Learning, University of Washington. Held at the Haas School of Business, University of California, Berkeley, the Data ...

NIPS 2017 Human-Computer Question Answering Competition: Intro

We're hosting a human-computer question answering competition at NIPS 2017. Submit your systems by October 2017!