Today!! AI in Practice - DSP, Audio, Speech, Languages

Please join us today!

Welcome to Amazon AWS AI in Practice!

With the goal of bringing together scientists, engineers, product managers, and entrepreneurs, everyone that is interested in Machine Learning is welcome!

This occurrence is jointly hosted with BISH Bash: https://www.meetup.com/bishbash/events/259737809/

Program:

12:00 — Lunch & Networking

1:15 — Opening remarks — Hassan Sawaf — Director of Applied Science

Talks:

1:30pm — Professor Hermann Ney — Chair of Human Language Technology and Pattern Recognition at RWTH Aachen University,
"Human Language Technology and Machine Learning."

2:30 — Kalpit Dixit — AWS Applied Scientist, Comprehend,
"Summarizing Text Articles."

3:00 — Julian Salazar — AWS Applied Scientist, Transcribe,
"Self-Attention and CTC for Scalable End-to-End Speech Recognition."

3:30 — Marcello Federico — AWS Principal Applied Scientist, Translate,
"Speech Translation Research through the IWSLT Workshop."

4:00 — Srikanth Tenneti — Post-Doc Scholar, Caltech DSP group & Amazon AI Fellowship Awardee,
"DSP-Inspired Deep Learning: A Case Study Using Ramanujan Subspaces."

4:30 — Break — Snacks & drinks

5:00 — Professor Demba Ba — Harvard University,
"Deeply-Sparse Signal Representations, Artificial Neural Networks and Hierarchical Processing in the Brain."

6:00 — Professor Paris Smaragdis — University of Illinois at Urbana-Champaign (UIUC),
"New Twists for New Tricks, Making Audio Deep Learning Practical."

7:00 — Dinner, Networking, Happy Hour, Announcements

9:00 — End of event

We are inviting submissions for future presentations at these events! Please send an abstract along with your LinkedIn account info to: valayna@amazon.com

Topics to cover include but not limited to:
— Computer Vision, Image & Video Processing
— Spoken Dialog Systems & Chat Bots
— Natural Language Processing & Machine Translation
— Speech Recognition & Transcription
— Content & Language Comprehension
— Audio, Music & Speech Processing
— Other applications of Machine Learning

Domains to cover include but not limited to:

— Retail & Industry
— AI for Good
— Media & Entertainment
— Healthcare & Medicine
— Society & Environment
— Security
— Education