Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Tackling the limits of deep learning

Richard Socher (Salesforce)
9:05am9:20am Wednesday, June 28, 2017
Location: Grand Ballroom
Secondary topics:  Deep Learning, Natural Language
Average rating: ***..
(3.67, 9 ratings)

What you'll learn

  • Explore how Salesforce delivers seamless and scalable AI to its customers

Description

AI presents a huge opportunity for businesses to personalize and improve customer experiences and improve efficiency, but the technical complexity of AI puts it out of reach for most companies. Richard Socher explains how Salesforce is doing the heavy lifting to deliver seamless and scalable AI to its customers. Fueled by state-of-the-art product innovation and breakthrough AI research, Salesforce is bringing together machine learning, deep learning, natural language processing, and computer vision to embed AI where people work. Along the way, Richard covers real-world examples, such as service agents that can proactively solve cases and create a more personalized connection with customers and marketers, and retailers that can create one-to-one email campaigns.

Photo of Richard Socher

Richard Socher

Salesforce

Richard Socher is chief scientist at Salesforce, where he leads the company’s research efforts and works on bringing state-of-the-art artificial intelligence solutions to Salesforce. Previously, Richard was the CEO and founder of MetaMind (acquired by Salesforce in April 2016). MetaMind’s deep learning AI platform analyzes, labels, and makes predictions on image and text data so businesses can make smarter, faster, and more accurate decisions than ever before. He was awarded the Distinguished Application Paper Award at the International Conference on Machine Learning (ICML) 2011, the 2011 Yahoo Key Scientific Challenges Award, a Microsoft Research PhD fellowship, a “Magic Grant” from the Brown Institute for Media Innovation, and the 2014 GigaOM Structure Award. Richard holds a PhD in deep learning from Stanford, where he worked with Chris Manning and Andrew Ng. His research won the Best Stanford CS PhD Thesis award.