O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

O'Reilly Artificial Intelligence Conference 2019 - San Jose, California

Video Description

The O’Reilly Artificial Intelligence Conference San Jose 2019 was some of the world’s top AI practitioners sharing their AI passion and AI knowledge with thousands of attendees. It was Uber AI Lab’s Kenneth Stanley illuminating the future of AI with his talk about open-endedness learning. It was Danny Lange (Unity Technologies) on game environments that test the capabilities of AI-trained agents; Yi Zhang (University of California, Santa Cruz) on chatbots and the nearness of true conversational computing; and Hagay Lupesko (Facebook) on the challenges of mega-scale, deep learning-based personalization modeling. In short, AI San Jose 2019 was a mind-blower and this video compilation gives you access to virtually all of it with hours of material to peruse, study, and absorb on your own schedule.

Highlights include:

  • Complete video recordings of the best of AI San Jose 2019’s keynote addresses, deep dive tutorials, and technical sessions.
  • Keynote addresses from AI thought leaders such as Andrew Feldman (Cerebras Systems), Sahika Genc (AWS DeepRacer/SageMaker RL), and Mike Jordan (UC Berkeley).
  • Unrestricted access to the exclusive AI Business Summit’s executive briefings, best practice sessions, and tutorials led by AI business pros such as Michael Radwin (Intuit), Bahman Bahmani (Rakuten), Mayukh Bhaowal (Salesforce Einstein), Yael Gozin (Pfizer), and James Manyika (McKinsey & Company).
  • Deep dive tutorials, including Jason Dai (Intel) on building deep learning apps for big data with the Analytics Zoo AI platform; Chaoran Yu (Lightbend) on doing machine learning (ML) with Kafka-based streaming pipelines; and Justina Petraityte (Rasa) on developing intelligent AI assistants based entirely on ML with open source Rasa NLU and Rasa Core.
  • Sessions devoted to AI Implementation, such as Anuradha Gali (Uber) on using AI to leverage 15 million trips a day on the Uber platform; Roshan Sumbaly (Facebook) on connecting the dots between the software engineering and ML development worlds; Paige Bailey’s (Google) on TensorFlow 2.0's new features; and Alex Ratner (Snorkel) on building and managing training datasets for ML with open source Snorkel.
  • Sessions focused on AI Models & Methods, including Lukas Biewald (Weights & Biases) review of how to use Keras to classify text with LSTMs and other ML techniques; and Francesca Lazzeri (Microsoft) on using AutoML to automate ML model selection and hyperparameter tuning.
  • Dozens of how-to-do-it sessions detailing the technologies, personnel, and processes required to move AI from a science project to a real business application.

Table of Contents

  1. Keynotes
    1. Highlights from the Keynotes of Artificial Intelligence Conference, San Jose 2019 00:29:02
    2. Building and deploying AI applications and systems at scale - Ben Lorica (O'Reilly Media), Roger Chen (Computable) 00:09:41
    3. Getting from A to AI - Eric Gardner (Intel) 00:12:51
    4. Unlocking the value of your data (sponsored by IBM Watson) - Dinesh Nirmal (IBM) 00:09:25
    5. Developing AI responsibly - Sarah Bird (Microsoft) 00:14:30
    6. The moral responsibility of AI builders (sponsored by Dataiku) - Triveni Gandhi (Dataiku) 00:05:06
    7. Enabling AI’s potential through wafer-scale integration - Andrew Feldman (Cerebras Systems) 00:13:16
    8. Going beyond supervised learning - Srinivas Narayanan (Facebook AI) 00:14:54
    9. AI for Ophthalmology: Doing What Doctors Can’t (sponsored by Dell Technologies) - Daniel Russakoff, Ph.D. (Voxeleron) 00:04:47
    10. On gradient-based methods for finding game-theoretic equilibria - Michael Jordan (UC Berkeley) 00:14:58
    11. Accelerate with Purpose - Ananth Sankaranarayanan (Intel) 00:09:50
    12. Practical insights into deep reinforcement learning - Sahika Genc (Amazon) 00:11:40
    13. Open-endedness: A new grand challenge for AI - Kenneth Stanley (Uber AI Labs | University of Central Florida) 00:18:09
    14. Safe and smarter driving, powered by AI (sponsored by Amazon Web Services) - Lei Pan (Nauto) 00:09:42
  2. Sponsored
    1. Getting Through the Ground Truth Grind (sponsored by iMerit) - Sina Bari (iMerit) 00:38:44
    2. Human Centered Machine Learning (sponsored by H2O.ai) - Navdeep Gill (H2O.ai) 00:31:40
    3. The holy grail of data science: Rapid model development and deployment (sponsored by Zepl) - Louis Huard (Zepl), Moon Soo Lee (Zepl | Apache Zeppelin) 00:32:42
    4. Unlock your data's value with AI (sponsored by HPE) - Pankaj Goyal (Hewlett Packard Enterprise), Nanda Vijaydev (Hewlett Packard Enterprise) 00:35:43
    5. 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) 00:38:29
    6. The race to 10,000 data scientists deploying 1,000,000 models (sponsored by Dataiku) - Kurt Muehmel (Dataiku) 00:36:54
    7. Talent for AI transformation: Building a strong AI team for the future (sponsored by TalentSeer) - Margaret Laffan (TalentSeer | BoomingStar Ventures) 00:40:18
    8. Build, train, and deploy predictive maintenance models at industrial scale (sponsored by Amazon Web Services) - Sunil Mallya (Amazon Web Services) 00:38:28
    9. Framing business problems as machine learning (ML) problems (sponsored by Amazon Web Services) - Carlos Escapa (Amazon Web Services) 00:39:23
    10. Automatic machine learning for the enterprise with H2O.ai Driverless AI (sponsored by H2O.ai) - Arno Candel (H2O.ai) 00:43:41
    11. The challenges and opportunities of augmented intelligence at scale (sponsored by Jumio) - Labhesh Patel (Jumio) 00:39:54
  3. AI Business Summit
    1. AI and deep learning enable 4x faster scans and productivity gains for clinical radiology - Enhao Gong (Subtle Medical), Greg Zaharchuk (Stanford University) 00:33:14
    2. Executive Briefing: Managing AI products - Mayukh Bhaowal (Salesforce) 00:39:13
    3. A framework for human-AI integration in the enterprise - Bahman Bahmani (Rakuten) 00:40:24
    4. Robot 2.0: Deep reinforcement learning for industrial robotics - Bastiane Huang (OSARO) 00:35:58
    5. Executive Briefing: Usable machine learning—Lessons from Stanford and beyond - Peter Bailis (Sisu | Stanford University) 00:48:30
    6. From bits to bedside: Translating routine clinical data into precision mammography - Dexter Hadley (University of California, San Francisco) 00:35:13
    7. Executive Briefing: What you must know to build AI systems that understand natural language - David Talby (Pacific AI) 00:38:17
    8. Executive Briefing: 5G—A playground for AI - Mazin Gilbert (AT&T Research) 00:56:35
    9. Executive Briefing: Similar but different—Delivering software with AI - Jana Eggers (Nara Logics) 00:44:41
    10. A framework to bootstrap and scale a machine learning function - Madhura Dudhgaonkar (Hiring) (Workday Inc.) 00:37:19
    11. Data science + design thinking: A perfect blend to achieve the best user experience - Michael Radwin (Intuit) 00:39:29
    12. Executive Briefing: Explaining Machine Learning Models - Ankur Taly (Fiddler labs) 00:30:33
    13. Repeatable AI-driven digital transformation: Insights from 1,000 projects - Debo Olaosebikan (Gigster) 00:38:44
    14. Artificial Intelligence Social Influence Model and Migration Paths: Implications to Institutions, Government and Businesses - Loretta Cheeks (Strong TIES) 00:41:48
    15. Executive Briefing: Unpacking AutoML - Paco Nathan (derwen.ai) 00:44:42
    16. Executive Briefing: An age of embeddings - Mayank Kejriwal (USC Information Sciences Institute) 00:44:59
  4. Interacting with AI
    1. Future Challenges in Human Language Understanding - Yishay Carmiel (IntelligentWire) 00:36:51
    2. Live coding a self-driving car (without a car) - Paris Buttfield-Addison (Secret Lab), Mars Geldard (University of Tasmania), Tim Nugent (lonely.coffee) 00:39:32
    3. Artificial and human intelligence in healthcare - Maithra Raghu (Cornell University/Google Brain) 00:41:35
    4. Can an AI assistant be as important as the web or as mobile? - Adam Cheyer (Samsung) 00:39:38
    5. Mozart in the box: Interacting with AI tools for music creation - Alessandro Palladini (Music Tribe) 00:39:06
  5. Implementing AI
    1. Scaling AI at Cerebras - Urs Köster (Cerebras Systems) 00:39:12
    2. Building and managing training datasets for ML with Snorkel - Alex Ratner (Snorkel) 00:36:45
    3. Scaling AI experiences at Facebook with PyTorch - Joseph Spisak (Facebook), Hao Lu (Facebook) 00:42:22
    4. Running large-scale machine learning experiments in the cloud - Shashank Prasanna (Amazon Web Services) 00:38:58
    5. Applying AI to secure the payments ecosystem - Chiranjeet Chetia (Visa), Shubham Agrawal (Visa) 00:41:56
    6. Are we deployed yet? Turning AI research into a revenue engine - Manasi Vartak (Verta.ai) 00:32:56
    7. Generative models for fixing image defects - Akhilesh Kumar (Adobe) 00:33:03
    8. Data distribution search: Deep reinforcement learning to improvise input datasets - Vijay Gabale (Infilect) 00:40:07
    9. Unshattering the mirror: Defragmenting the deep learning ecosystem - Evan Sparks (Determined AI) 00:46:11
    10. Building autonomous network operation using deep learning and AI - Jisheng Wang (Mist) 00:40:58
    11. Unlocking the next stage in computer vision with deep neural networks - Josh Weisberg (Zillow Group) 00:41:00
    12. Talking to the machines: Monitoring production machine learning systems - Ting-Fang Yen (DataVisor) 00:29:14
    13. Improving OCR quality of documents using generative adversarial networks - Nagendra Shishodia (EXL), Solmaz Torabi (EXL), Chaithanya Manda (EXL) 00:37:16
    14. Introducing Kubeflow (with special guests TensorFlow and Apache Spark) - Holden Karau (Google), Trevor Grant (IBM) 00:33:47
    15. Industrialized capsule networks for text analytics - Vijay Agneeswaran (Walmart Labs), Abhishek Kumar (Publicis Sapient) 00:36:43
    16. Deep learning on mobile - Meher Kasam (Square) 00:43:19
    17. Supercharging business decisions with AI: Insight, optimize, and personalize to save $100M - Anuradha Gali (Uber) 00:42:25
    18. Getting started with TensorFlow 2.0 - Paige Bailey (Google) 00:41:15
    19. Reference architectures for AI and machine learning - Mathew Salvaris (Microsoft), Angus Taylor (Microsoft) 00:30:05
    20. Challenges and future directions in deploying NLP in commercial environments - Moshe Wasserblat (Intel) 00:39:04
    21. Lessons from building Facebook's visual cortex - Roshan Sumbaly (Facebook) 00:40:30
    22. Personalization at scale: Challenges and practical techniques - Hagay Lupesko (Facebook) 00:37:26
    23. Deep learning coming to the tire industry: Warehouse Staffing with RNN-LSTMs and Pricing Optimizations with DNNs - Alex (Tianchu) Liang (American Tire Distributors) 00:34:18
    24. Swift for TensorFlow: A next-generation framework for differential programming - Brennan Saeta (Google) 00:39:49
    25. How to leverage powerful Intel-based instance types to create new solutions - Carlos Escapa (Amazon Web Services) 00:16:48
    26. Uber’s deep learning applications in NLP and conversational AI - Huaixiu Zheng (Uber) 00:41:14
    27. The OS for AI: How serverless computing enables the next gen of machine learning - Jonathan Peck (Algorithmia) 00:36:42
    28. Recommendation systems challenges at Twitter scale - Ashish Bansal (Twitter) 00:39:02
  6. Models and Methods
    1. PyTorch at scale for translation and NLP - Stef Nelson-Lindall (Facebook) 00:28:10
    2. Interpreting millions of patient stories with deep learned OCR and NLP - Stacy Ashworth (SelectData), Alberto Andreotti (John Snow Labs) 00:39:34
    3. Using deep learning models to extract the most value from 360-degree images - Shourabh Rawat (Trulia) 00:32:16
    4. Named entity recognition at scale with deep learning - Sijun He (Twitter), Ali Mollahosseini (Twitter) 00:34:00
    5. Can behavioral analytics for enterprise security benefit from approaches in NLP? - Ramsundar Janakiraman (Aruba Networks, A HPE Company) 00:40:44
    6. Self-supervised machine learning, the next big thing in AI - Vinay Rao (RocketML), Santi Adavani (RocketML) 00:42:08
    7. Sequence to sequence modeling for time series forecasting - Arun Kejariwal (Independent), Ira Cohen (Anodot) 00:44:53
    8. Transfer learning NLP: Machine reading comprehension for question answering - Anusua Trivedi (Microsoft) 00:41:51
    9. Machine learning for autonomous driving: Recent advances and future challenges - Li Erran Li (Scale AI | Columbia University) 00:39:25
    10. Using automated machine learning for hyperparameter optimization and algorithm selection - Francesca Lazzeri (Microsoft) 00:39:37
    11. Long-term real-time network traffic flow prediction using LSTM recurrent neural network - Wei Cai (Cox Communications) 00:37:35
    12. A practical guide toward explainability and bias evaluation in AI and machine learning - Alejandro Saucedo (The Institute for Ethical AI & Machine Learning) 00:40:39
    13. Delivering AI vision ecosystem offers with Intel AI: In Production - Lindsay Hiebert (Intel) 00:39:36
    14. Fighting crime with graph learning - Mark Weber (MIT-IBM Watson AI Lab) 00:50:57
  7. R&D and Innovation Sponsored by: Intuit
    1. Toward universal semantic understanding of natural languages - Huaiyu Zhu (IBM Research - Almaden), Dulce Ponceleon (IBM Research - Almaden), Yunyao Li (IBM Research - Almaden) 00:37:11
    2. Development and application of advanced AI decision making for manufacturing - Vadim Pinskiy (Nanotronics) 00:36:34
    3. AI for cell shaping in mobile networks - Julien Forgeat (Ericsson) 00:39:55
    4. Creating autonomy for social robots - Dylan Glas (Futurewei Technologies), Phoebe Liu (Figure Eight) 00:41:26
    5. Data science without seeing the data: Advanced encryption to the rescue - Tzvika Barenholz (Intuit), Induprakas Keri (Intuit) 00:38:24
    6. Language inference in medicine - Chaitanya Shivade (IBM Research) 00:32:43
    7. Software toolchain for the hybrid digital-analog, memristor-based accelerator for machine learning - Dejan Milojicic (Hewlett Packard Laboratories) 00:41:18
  8. AI in the Enterprise: The Intel® AI Builders Showcase Event
    1. Welcome and AIB overview and growth: Impact on AI ecosystem - Brigitte Alexander (Intel) 00:07:24
    2. Driving business impact with the Intel AI technology portfolio - Ananth Sankar (Intel) 00:10:24
    3. Clinical deployment of radiology AI powered by OpenVINO - Liren Zhu (Subtle Medical) 00:05:22
    4. Industrialize AI with Cloudera - Jessie Lin (Cloudera) 00:07:21
    5. Automated time series forecasting and optimization for enterprises - Yuan Shen (OneClick.ai) 00:08:09
    6. QuEST vision analytics solution with OpenVINO and Intel AI - Rubayat Mahmud (QuEST Global) 00:10:38
    7. Saving Antarctic penguins with deep learning - Ganes Kesari (Gramener) 00:12:32
    8. Pipe Sleuth: AI-based pipeline assessment - Sundar Varadarajan (Wipro) 00:10:56
    9. AI-based container usage optimization tool - Amine Kerkeni (InstaDeep) 00:10:04
    10. The turnkey high-compliance AI platform - David Talby (Pacific AI) 00:10:43
    11. Accelerating AI from research to production in the enterprise - Ari Kamlani (Skymind) 00:12:05
    12. Running enterprise IT more efficiently, improving customer experience, and increasing the agility and stability of IT - Anjali Gajendragadkar (Digitate) 00:14:35
    13. Using AI to accelerate time to customer - Derek Wang (Stratifyd) 00:04:59
    14. Data analytics at the retail edge - Han Yang (Cisco) 00:06:12
    15. Accelerate innovation with DevOps-like agility for machine learning pipelines - Nanda Vijaydev (Hewlett Packard Enterprise) 00:14:46
    16. Accelerating deep learning workloads in the cloud and data centers - Ravi Panchumarthy (Intel) 00:16:48
    17. Intel-based AI solutions from cloud to edge - Alan Chang (Inspur) 00:09:53
    18. Lenovo intelligent computing orchestration - Matt Ziegler (Lenovo) 00:10:15
    19. Dell Ready Solutions - Phlip Hummel (Dell EMC) 00:10:48
    20. Closing Remarks - Brigitte Alexander (Intel) 00:02:43
  9. Case Studies
    1. Advancing our understanding of deep reinforcement learning with community-driven insights - Danny Lange (Unity Technologies) 00:41:02
    2. Improving revenue cycle management with deep learning: A healthcare case study - Sanji Fernando (Optum) 00:47:19
    3. Computer vision and deep OCR in the enterprise: 3 use cases - Dave Ferrell (Dynam.AI) 00:30:31
    4. Environmental AI: Using machine learning to address mosquito-borne diseases - Leslie De Jesus (Wovenware) 00:33:06
  10. Tutorials
    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 1 00:45:44
    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 2 00:51:37
    3. 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 00:55:29
    4. Putting cutting-edge modern NLP into practice - Joel Grus (Allen Institute for Artificial Intelligence) - Part 1 00:57:05
    5. Putting cutting-edge modern NLP into practice - Joel Grus (Allen Institute for Artificial Intelligence) - Part 2 00:50:31
    6. Putting cutting-edge modern NLP into practice - Joel Grus (Allen Institute for Artificial Intelligence) - Part 3 00:51:18
    7. Using Keras to classify text with LSTMs and other ML techniques - Lukas Biewald (Weights & Biases) - Part 1 00:50:24
    8. Using Keras to classify text with LSTMs and other ML techniques - Lukas Biewald (Weights & Biases) - Part 2 1:01:37
    9. Using Keras to classify text with LSTMs and other ML techniques - Lukas Biewald (Weights & Biases) (Clone) 00:57:08
    10. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 1 00:46:01
    11. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 2 00:47:58
    12. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 3 00:44:51
    13. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 4 00:45:00
    14. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 5 00:45:27
    15. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 6 00:45:06
    16. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 7 00:45:26
    17. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 8 00:52:03
    18. Herding cats: Product management in the machine learning era - Ira Cohen (Anodot) - Part 1 00:50:09
    19. Herding cats: Product management in the machine learning era - Ira Cohen (Anodot) - Part 2 00:58:50
    20. Herding cats: Product management in the machine learning era - Ira Cohen (Anodot) - Part 3 00:52:43
    21. Getting started with Kubeflow - Skyler Thomas (MapR) - Part 1 00:45:14
    22. Getting started with Kubeflow - Skyler Thomas (MapR) - Part 2 00:45:16
    23. Getting started with Kubeflow - Skyler Thomas (MapR) - Part 3 00:47:35
    24. Getting started with Kubeflow - Skyler Thomas (MapR) - Part 4 00:47:19
    25. Getting started with PyTorch - Mo Patel (Independent) - Part 1 00:48:31
    26. Getting started with PyTorch - Mo Patel (Independent) - Part 2 00:48:08
    27. Getting started with PyTorch - Mo Patel (Independent) (Clone) 00:50:51
    28. Design thinking for AI - Chris Butler (IPsoft) - Part 1 00:54:09
    29. Design thinking for AI - Chris Butler (IPsoft) - Part 2 00:46:44
    30. Design thinking for AI - Chris Butler (IPsoft) - Part 3 00:38:08
    31. Hands-on machine learning with Kafka-based streaming pipelines - Boris Lublinsky (Lightbend), Chaoran Yu (Lightbend) - Part 1 00:50:03
    32. Hands-on machine learning with Kafka-based streaming pipelines - Boris Lublinsky (Lightbend), Chaoran Yu (Lightbend) - Part 2 00:54:20
    33. Hands-on machine learning with Kafka-based streaming pipelines - Boris Lublinsky (Lightbend), Chaoran Yu (Lightbend) - Part 3 00:59:01
    34. Going beyond FAQ assistants with machine learning and open source tools - Justina Petraityte (Rasa) - Part 1 00:41:14
    35. Going beyond FAQ assistants with machine learning and open source tools - Justina Petraityte (Rasa) - Part 2 00:49:06
    36. Going beyond FAQ assistants with machine learning and open source tools - Justina Petraityte (Rasa) - Part 3 00:35:36
    37. Going beyond FAQ assistants with machine learning and open source tools - Justina Petraityte (Rasa) - Part 4 00:43:08