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Table of Contents

Keynotes

Highlights from the Keynotes of Artificial Intelligence Conference, San Jose 2019

Building and deploying AI applications and systems at scale - Ben Lorica (O’Reilly Media), Roger Chen (Computable)

Getting from A to AI - Eric Gardner (Intel)

Unlocking the value of your data (sponsored by IBM Watson) - Dinesh Nirmal (IBM)

Developing AI responsibly - Sarah Bird (Microsoft)

The moral responsibility of AI builders (sponsored by Dataiku) - Triveni Gandhi (Dataiku)

Enabling AI’s potential through wafer-scale integration - Andrew Feldman (Cerebras Systems)

Going beyond supervised learning - Srinivas Narayanan (Facebook AI)

AI for Ophthalmology: Doing What Doctors Can’t (sponsored by Dell Technologies) - Daniel Russakoff, Ph.D. (Voxeleron)

On gradient-based methods for finding game-theoretic equilibria - Michael Jordan (UC Berkeley)

Accelerate with Purpose - Ananth Sankaranarayanan (Intel)

Practical insights into deep reinforcement learning - Sahika Genc (Amazon)

Open-endedness: A new grand challenge for AI - Kenneth Stanley (Uber AI Labs | University of Central Florida)

Safe and smarter driving, powered by AI (sponsored by Amazon Web Services) - Lei Pan (Nauto)

Sponsored

Getting Through the Ground Truth Grind (sponsored by iMerit) - Sina Bari (iMerit)

Human Centered Machine Learning (sponsored by H2O.ai) - Navdeep Gill (H2O.ai)

The holy grail of data science: Rapid model development and deployment (sponsored by Zepl) - Louis Huard (Zepl), Moon Soo Lee (Zepl | Apache Zeppelin)

Unlock your data’s value with AI (sponsored by HPE) - Pankaj Goyal (Hewlett Packard Enterprise), Nanda Vijaydev (Hewlett Packard Enterprise)

Operationalize AI at scale: From drift detection to monitoring the business impact of AI (sponsored by IBM Watson) - Manish Bhide (IBM Watson), Rohan Vaidyanathan (IBM Watson)

The race to 10,000 data scientists deploying 1,000,000 models (sponsored by Dataiku) - Kurt Muehmel (Dataiku)

Talent for AI transformation: Building a strong AI team for the future (sponsored by TalentSeer) - Margaret Laffan (TalentSeer | BoomingStar Ventures)

Making reinforcement learning practical for real-world developers (sponsored by Amazon Web Services) - Sunil Mallya (Amazon Web Services)

Build, train, and deploy predictive maintenance models at industrial scale (sponsored by Amazon Web Services) - Sunil Mallya (Amazon Web Services)

Framing business problems as machine learning (ML) problems (sponsored by Amazon Web Services) - Carlos Escapa (Amazon Web Services)

Automatic machine learning for the enterprise with H2O.ai Driverless AI (sponsored by H2O.ai) - Arno Candel (H2O.ai)

The challenges and opportunities of augmented intelligence at scale (sponsored by Jumio) - Labhesh Patel (Jumio)

AI Business Summit

AI and deep learning enable 4x faster scans and productivity gains for clinical radiology - Enhao Gong (Subtle Medical), Greg Zaharchuk (Stanford University)

Executive Briefing: Managing AI products - Mayukh Bhaowal (Salesforce)

A framework for human-AI integration in the enterprise - Bahman Bahmani (Rakuten)

Robot 2.0: Deep reinforcement learning for industrial robotics - Bastiane Huang (OSARO)

Executive Briefing: Usable machine learning—Lessons from Stanford and beyond - Peter Bailis (Sisu | Stanford University)

From bits to bedside: Translating routine clinical data into precision mammography - Dexter Hadley (University of California, San Francisco)

Executive Briefing: What you must know to build AI systems that understand natural language - David Talby (Pacific AI)

Executive Briefing: 5G—A playground for AI - Mazin Gilbert (AT Research)

Executive Briefing: Similar but different—Delivering software with AI - Jana Eggers (Nara Logics)

A framework to bootstrap and scale a machine learning function - Madhura Dudhgaonkar (Hiring) (Workday Inc.)

Data science + design thinking: A perfect blend to achieve the best user experience - Michael Radwin (Intuit)

Executive Briefing: Explaining Machine Learning Models - Ankur Taly (Fiddler labs)

Repeatable AI-driven digital transformation: Insights from 1,000 projects - Debo Olaosebikan (Gigster)

Artificial Intelligence Social Influence Model and Migration Paths: Implications to Institutions, Government and Businesses - Loretta Cheeks (Strong TIES)

Executive Briefing: Unpacking AutoML - Paco Nathan (derwen.ai)

Executive Briefing: An age of embeddings - Mayank Kejriwal (USC Information Sciences Institute)

Interacting with AI

Future Challenges in Human Language Understanding - Yishay Carmiel (IntelligentWire)

Live coding a self-driving car (without a car) - Paris Buttfield-Addison (Secret Lab), Mars Geldard (University of Tasmania), Tim Nugent (lonely.coffee)

Artificial and human intelligence in healthcare - Maithra Raghu (Cornell University/Google Brain)

Can an AI assistant be as important as the web or as mobile? - Adam Cheyer (Samsung)

Mozart in the box: Interacting with AI tools for music creation - Alessandro Palladini (Music Tribe)

Implementing AI

Scaling AI at Cerebras - Urs Köster (Cerebras Systems)

Building and managing training datasets for ML with Snorkel - Alex Ratner (Snorkel)

Scaling AI experiences at Facebook with PyTorch - Joseph Spisak (Facebook), Hao Lu (Facebook)

Running large-scale machine learning experiments in the cloud - Shashank Prasanna (Amazon Web Services)

Applying AI to secure the payments ecosystem - Chiranjeet Chetia (Visa), Shubham Agrawal (Visa)

Are we deployed yet? Turning AI research into a revenue engine - Manasi Vartak (Verta.ai)

Generative models for fixing image defects - Akhilesh Kumar (Adobe)

Data distribution search: Deep reinforcement learning to improvise input datasets - Vijay Gabale (Infilect)

Unshattering the mirror: Defragmenting the deep learning ecosystem - Evan Sparks (Determined AI)

Building autonomous network operation using deep learning and AI - Jisheng Wang (Mist)

Unlocking the next stage in computer vision with deep neural networks - Josh Weisberg (Zillow Group)

Talking to the machines: Monitoring production machine learning systems - Ting-Fang Yen (DataVisor)

Improving OCR quality of documents using generative adversarial networks - Nagendra Shishodia (EXL), Solmaz Torabi (EXL), Chaithanya Manda (EXL)

Introducing Kubeflow (with special guests TensorFlow and Apache Spark) - Holden Karau (Google), Trevor Grant (IBM)

Industrialized capsule networks for text analytics - Vijay Agneeswaran (Walmart Labs), Abhishek Kumar (Publicis Sapient)

Deep learning on mobile - Meher Kasam (Square)

Supercharging business decisions with AI: Insight, optimize, and personalize to save $100M - Anuradha Gali (Uber)

Getting started with TensorFlow 2.0 - Paige Bailey (Google)

Reference architectures for AI and machine learning - Mathew Salvaris (Microsoft), Angus Taylor (Microsoft)

Challenges and future directions in deploying NLP in commercial environments - Moshe Wasserblat (Intel)

Lessons from building Facebook’s visual cortex - Roshan Sumbaly (Facebook)

Personalization at scale: Challenges and practical techniques - Hagay Lupesko (Facebook)

Deep learning coming to the tire industry: Warehouse Staffing with RNN-LSTMs and Pricing Optimizations with DNNs - Alex (Tianchu) Liang (American Tire Distributors)

Swift for TensorFlow: A next-generation framework for differential programming - Brennan Saeta (Google)

How to leverage powerful Intel-based instance types to create new solutions - Carlos Escapa (Amazon Web Services)

Uber’s deep learning applications in NLP and conversational AI - Huaixiu Zheng (Uber)

The OS for AI: How serverless computing enables the next gen of machine learning - Jonathan Peck (Algorithmia)

Recommendation systems challenges at Twitter scale - Ashish Bansal (Twitter)

Models and Methods

PyTorch at scale for translation and NLP - Stef Nelson-Lindall (Facebook)

Interpreting millions of patient stories with deep learned OCR and NLP - Stacy Ashworth (SelectData), Alberto Andreotti (John Snow Labs)

Using deep learning models to extract the most value from 360-degree images - Shourabh Rawat (Trulia)

Named entity recognition at scale with deep learning - Sijun He (Twitter), Ali Mollahosseini (Twitter)

Can behavioral analytics for enterprise security benefit from approaches in NLP? - Ramsundar Janakiraman (Aruba Networks, A HPE Company)

Self-supervised machine learning, the next big thing in AI - Vinay Rao (RocketML), Santi Adavani (RocketML)

Sequence to sequence modeling for time series forecasting - Arun Kejariwal (Independent), Ira Cohen (Anodot)

Transfer learning NLP: Machine reading comprehension for question answering - Anusua Trivedi (Microsoft)

Machine learning for autonomous driving: Recent advances and future challenges - Li Erran Li (Scale AI | Columbia University)

Using automated machine learning for hyperparameter optimization and algorithm selection - Francesca Lazzeri (Microsoft)

Long-term real-time network traffic flow prediction using LSTM recurrent neural network - Wei Cai (Cox Communications)

A practical guide toward explainability and bias evaluation in AI and machine learning - Alejandro Saucedo (The Institute for Ethical AI Machine Learning)

Delivering AI vision ecosystem offers with Intel AI: In Production - Lindsay Hiebert (Intel)

Fighting crime with graph learning - Mark Weber (MIT-IBM Watson AI Lab)

R and Innovation Sponsored by: Intuit

Toward universal semantic understanding of natural languages - Huaiyu Zhu (IBM Research - Almaden), Dulce Ponceleon (IBM Research - Almaden), Yunyao Li (IBM Research - Almaden)

Development and application of advanced AI decision making for manufacturing - Vadim Pinskiy (Nanotronics)

AI for cell shaping in mobile networks - Julien Forgeat (Ericsson)

Creating autonomy for social robots - Dylan Glas (Futurewei Technologies), Phoebe Liu (Figure Eight)

Data science without seeing the data: Advanced encryption to the rescue - Tzvika Barenholz (Intuit), Induprakas Keri (Intuit)

Language inference in medicine - Chaitanya Shivade (IBM Research)

Software toolchain for the hybrid digital-analog, memristor-based accelerator for machine learning - Dejan Milojicic (Hewlett Packard Laboratories)

AI in the Enterprise: The Intel® AI Builders Showcase Event

Welcome and AIB overview and growth: Impact on AI ecosystem - Brigitte Alexander (Intel)

Driving business impact with the Intel AI technology portfolio - Ananth Sankar (Intel)

Clinical deployment of radiology AI powered by OpenVINO - Liren Zhu (Subtle Medical)

Industrialize AI with Cloudera - Jessie Lin (Cloudera)

Automated time series forecasting and optimization for enterprises - Yuan Shen (OneClick.ai)

QuEST vision analytics solution with OpenVINO and Intel AI - Rubayat Mahmud (QuEST Global)

Saving Antarctic penguins with deep learning - Ganes Kesari (Gramener)

Pipe Sleuth: AI-based pipeline assessment - Sundar Varadarajan (Wipro)

AI-based container usage optimization tool - Amine Kerkeni (InstaDeep)

The turnkey high-compliance AI platform - David Talby (John Snow Labs)

Accelerating AI from research to production in the enterprise - Ari Kamlani (Skymind)

Running enterprise IT more efficiently, improving customer experience, and increasing the agility and stability of IT - Anjali Gajendragadkar (Digitate)

Using AI to accelerate time to customer - Derek Wang (Stratifyd)

Data analytics at the retail edge - Han Yang (Cisco)

Accelerate innovation with DevOps-like agility for machine learning pipelines - Nanda Vijaydev (Hewlett Packard Enterprise)

Accelerating deep learning workloads in the cloud and data centers - Ravi Panchumarthy (Intel)

Intel-based AI solutions from cloud to edge - Alan Chang (Inspur)

Lenovo intelligent computing orchestration - Matt Ziegler (Lenovo)

Dell Ready Solutions - Phlip Hummel (Dell EMC)

Closing Remarks - Brigitte Alexander (Intel)

Case Studies

Advancing our understanding of deep reinforcement learning with community-driven insights - Danny Lange (Unity Technologies)

Improving revenue cycle management with deep learning: A healthcare case study - Sanji Fernando (Optum)

Computer vision and deep OCR in the enterprise: 3 use cases - Dave Ferrell (Dynam.AI)

Environmental AI: Using machine learning to address mosquito-borne diseases - Leslie De Jesus (Wovenware)

Tutorials

Build a self-driving car without a car: ML problem-solving with a game engine - Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee), Mars Geldard (University of Tasmania) - Part 1

Build a self-driving car without a car: ML problem-solving with a game engine - Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee), Mars Geldard (University of Tasmania) - Part 2

Build a self-driving car without a car: ML problem-solving with a game engine - Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee), Mars Geldard (University of Tasmania) - Part 3

Putting cutting-edge modern NLP into practice - Joel Grus (Allen Institute for Artificial Intelligence) - Part 1

Putting cutting-edge modern NLP into practice - Joel Grus (Allen Institute for Artificial Intelligence) - Part 2

Putting cutting-edge modern NLP into practice - Joel Grus (Allen Institute for Artificial Intelligence) - Part 3

Using Keras to classify text with LSTMs and other ML techniques - Lukas Biewald (Weights Biases) - Part 1

Using Keras to classify text with LSTMs and other ML techniques - Lukas Biewald (Weights Biases) - Part 2

Using Keras to classify text with LSTMs and other ML techniques - Lukas Biewald (Weights Biases) (Clone)

Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 1

Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 2

Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 3

Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 4

Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 5

Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 6

Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 7

Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 8

Herding cats: Product management in the machine learning era - Ira Cohen (Anodot) - Part 1

Herding cats: Product management in the machine learning era - Ira Cohen (Anodot) - Part 2

Herding cats: Product management in the machine learning era - Ira Cohen (Anodot) - Part 3

Getting started with Kubeflow - Skyler Thomas (MapR) - Part 1

Getting started with Kubeflow - Skyler Thomas (MapR) - Part 2

Getting started with Kubeflow - Skyler Thomas (MapR) - Part 3

Getting started with Kubeflow - Skyler Thomas (MapR) - Part 4

Getting started with PyTorch - Mo Patel (Independent) - Part 1

Getting started with PyTorch - Mo Patel (Independent) - Part 2

Getting started with PyTorch - Mo Patel (Independent) (Clone)

Design thinking for AI - Chris Butler (IPsoft) - Part 1

Design thinking for AI - Chris Butler (IPsoft) - Part 2

Design thinking for AI - Chris Butler (IPsoft) - Part 3

Hands-on machine learning with Kafka-based streaming pipelines - Boris Lublinsky (Lightbend), Chaoran Yu (Lightbend) - Part 1

Hands-on machine learning with Kafka-based streaming pipelines - Boris Lublinsky (Lightbend), Chaoran Yu (Lightbend) - Part 2

Hands-on machine learning with Kafka-based streaming pipelines - Boris Lublinsky (Lightbend), Chaoran Yu (Lightbend) - Part 3

Going beyond FAQ assistants with machine learning and open source tools - Justina Petraityte (Rasa) - Part 1

Going beyond FAQ assistants with machine learning and open source tools - Justina Petraityte (Rasa) - Part 2

Going beyond FAQ assistants with machine learning and open source tools - Justina Petraityte (Rasa) - Part 3

Going beyond FAQ assistants with machine learning and open source tools - Justina Petraityte (Rasa) - Part 4