Where machine learning meets artificial intelligence. The rise of intelligent machines to make sense of data.

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Speakers

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Tara Sainath

Senior Research Scientist

Google

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Antoine Bordes

Research Scientist

Facebook AI Research

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Avneesh Saluja

Machine Learning Scientist

Airbnb

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Yan Karklin

Principal Data Scientist

Knewton

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Kamelia Aryafar

Senior Data Scientist

Etsy

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Kathryn Hume

President

Fast Forward Labs

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Carl Vondrick

PhD Student

MIT

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Rahel Jhirad

Director, Data Science

Hearst

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Arjun Krishnan

PhD Researcher

Princeton Uni

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Erik Andrejko

Head of Data Science

Climate Corporation

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Joanna Bryson

Professor

University of Bath

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Sravan Puttagunta

CEO

Civil Maps

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Olexandr Isayev

Research Scientist

University of North Carolina

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Shaunak Khire

Founder

EmmaAI

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Soumith Chintala

Research Engineer

Facebook

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Ethan Rosenthal

Data Scientist

Dia&Co

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Johann Hauswald

Co-Founder & VP, Engineering

Clinc

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Dan Shiebler

Data Scientist

TrueMotion

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Alexander Statnikov

VP of Digital Modeling & ML

American Express

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Hanlin Tang

Staff Algorithms Engineer

Intel

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Steve Ardire

Strategic Advisor

Startups

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Marc Gyongyosi

CEO & Founder

IFM Technologies

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Luis Oros

Product Development Manager

Pearson

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Ed Chow

Programme Manager, Jet Propulsion Lab

NASA

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Stephen Scarr

CEO

Info.com & eContext

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Devin Gaffney

PhD Student

Northeastern Universtiy

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Kadija Ferryman

Postdoctoral Researcher

Data & Society

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Rohan Agrawal

Machine Learning Engineer

Spotify

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Alejandro Jaimes

CTO & Chief Scientist

Acesio

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Lou Auguste

Founder & CEO

AlexaPath

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Anasse Bari

Clinical Assistant Professor of Computer Science

NYU

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Max Sklar

Machine Laarning Engineer

Foursquare

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Jalak Jobanputra

Founder & Managing Partner

Future\Perfect Ventures

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Ben Klein

Applied Researcher

eBay

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New research from FAIR, Facebook’s artificial intelligence research arm, might help to point the way. Last year the team introduced a new type of machine-learning model for language understanding, called “memory networks.” The idea was to combine machine learning algorithms with a sort of working memory, letting bots store and retrieve information in a way that’s relevant to a given conversation.

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In a large scale experiment using data from the online marketplace Etsy, we verify that moving to a multimodal representation significantly improves ranking quality. We show how image features can capture fine-grained style information not available in a text-only representation. In addition, we show concrete examples of how image information can successfully disentangle pairs of highly different

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Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have made an important new breakthrough in predictive vision, developing an algorithm that can anticipate interactions more accurately than ever before.

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A study published today in Nature aims to change this by using a big data and machine learning approach to make a genome-wide prediction of Autism spectrum disorder (ASD) genes. The first author of the study, Arjun Krishnan, told us how their results could help with the early diagnosis and treatment of ASD.

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New research from computer scientists at Princeton suggests that computers learning human languages will also inevitably learn those human biases. In a draft paper, researchers describe how they used a common language-learning algorithm to infer associations between English words.

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“Civil Maps’s scalable map generation process enables fully autonomous vehicles to drive like humans do—identifying on-road and off-road features even when they might be missing, deteriorated, or hidden from view and letting a car know what it can expect along its route,” explained Civil Maps CEO Sravan Puttagunta.

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Automation is not new to Wall Street. But Shaunak Khire, Emma’s creator, claims his system differs from current finance computing — high-frequency trading and “quant” data science — because its system of neural nets takes into account a more complex set of factors affecting stocks, like management changes or monetary policy in Europe, that other programs miss.

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In our last post, we presented semantic classification as a force for organization. By classifying both search keywords and ecommerce products to a shared taxonomy of concepts, businesses can improve the shopping experience by matching searches to results, regardless of ambiguities in word choice on either side of the equation.

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Why Attend

Speakers

Extraordinary Speakers

Discover advances in machine learning and artificial intelligence from the world's leading innovators. Learn from the industry experts in speech & image recognition, natural language processing and computer vision. Explore how AI will impact transport, manufacturing, healthcare, retail and more.

Tech

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The summit will showcase the opportunities of advancing trends in machine learning and their applications in business and society. Machine intelligence is enabling applications including virtual assistants, robots to help assisted living, and identifying items in videos. What else can it solve?

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A unique opportunity to interact with industry leaders, influential technologists, data scientists & founders leading the machine intelligence revolution. Learn from & connect with 250+ industry innovators sharing best practices to advance machine learning and AI impact and opportunities.

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Who Should Attend

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Topics we cover

Machine Learning
Machine Learning
NLP
NLP
Drones
Drones
Deep Learning
Deep Learning
Speech Recognition
Speech Recognition
AI
AI
Pattern Recognition
Pattern Recognition
Robotics
Robotics

Confirmed Attendees Include

PwC
harvard uni
Google
Cogniance
groupon
Comcast
Fujitsu
booking.com
capital one
Travelers
horizon investments
axon vibe
citigroup
intento
sony
Accenture
Intel
samsung
sas
Apple

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