AI News, Ben Taskar artificial intelligence

AI in Public Sector: Tool for inclusion or exclusion?

March 1, 2018 Panel Event AI in the Public Sector: Tool for inclusion or exclusion? This panel was organized by the Taskar Center for Accessible Technology as ...

A Dose of A.I. - KQED QUEST

In this QUEST web exclusive, Stanford University computer science professor and artificial intelligence (A.I.) researcher Daphne Koller explains how A.I. can be ...

Playing with Prolog - Machine Learning with Structured Data

Prolog for machine learning? Yes! Machine Learning with structured data is pretty amazing. Sam gives us a run down on using ML with structured data, ...

Robust Semi-Supervised Learning

Semi-supervised learning algorithms are designed to learn an unknown concept from a partially-labeled data set of training examples. They are widely popular ...

UW CSE Distinguished Lecture: Pieter Abbeel (UC Berkeley)

Ben Taskar Memorial Lecture Deep Reinforcement Learning for Robotics Abstract Deep learning has enabled significant advances in supervised learning ...

Taskar Memorial Lecture 2018: M. Jordan (UC, Berkeley)

On Gradient-Based Optimization: Accelerated, Stochastic and Nonconvex Abstract: Many new theoretical challenges have arisen in the area of gradient-based ...

NW-NLP 2018: Semantic Matching Against a Corpus

The fifth Pacific Northwest Regional Natural Language Processing Workshop will be held on Friday April 27, 2018, in Redmond, WA. We accepted abstracts and ...

Arman Cohan: Text Summarization and Categorization for Scientific and Health-Related Data

Arman Cohan Text Summarization and Categorization for Scientific and Health-Related Data Abstract: The rapid growth of scientific literature has created a ...

Blake Richards: Deep Learning with Ensembles of Neocortical Microcircuits (ICLR 2018 invited talks)

Abstract: Deep learning in artificial intelligence (AI) has demonstrated that learning hierarchical representations is a good approach for generating useful ...

Computational Challenges and the Future of ML Panel

Panelists: Maryam Fazel (University of Washington), Yoav Freund (UC San Diego), Michael Jordan (UC Berkeley), Richard Karp (UC Berkeley), and Marina ...