conference

O'Reilly Artificial Intelligence Conference 2017 - San Francisco, CA

by O'Reilly Media, Inc.

September 2017

64h 28m

English

Closed Captioning available in German, English, Spanish, French, Japanese, Korean, Portuguese (Portugal, Brazil), Chinese (Simplified), Chinese (Traditional)

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Course outline

The inevitable merger of IQ and EQ in technology - Rana el Kaliouby (Affectiva)
13m 20s
Engineering the future of AI for businesses (sponsored by IBM Watson) - Ruchir Puri (IBM)
10m 36s
The state of AI adoption - Ben Lorica (O'Reilly Media), Roger Chen (.)
3m 59s
Deep learning to fight cancer: Fireside chat - Peter Norvig (Google), Abu Qader (GliaLab)
13m 16s
AI is the new electricity. - Andrew Ng (Coursera)
24m 47s
How to escape saddle points efficiently - Michael Jordan (UC Berkeley)
13m 31s
Why democratizing AI matters: Computing, data, algorithms, and talent - Jia Li (Google)
13m 42s
AI mimicking nature: Flying and talking (sponsored by Microsoft) - Lili Cheng (Microsoft)
11m 7s
Accelerating AI - Steve Jurvetson (DFJ)
14m 40s
Fireside chat with Naveen Rao and Steve Jurvetson - Naveen Rao (Intel), Steve Jurvetson (DFJ)
12m 17s
Build smart applications with your new super power: Cloud AI (sponsored by Google Cloud) - Philippe Poutonnet (Google)
5m 43s
Our Skynet moment - Tim O'Reilly (O'Reilly Media)
20m 43s
Scalable deep learning with Microsoft Cognitive Toolkit (sponsored by Microsoft) - Anusua Trivedi (Microsoft)
42m 25s
Pushing the boundaries of ML using TensorFlow and Google Cloud (sponsored by Google Cloud) - Magnus Hyttsten (Google)
40m 20s
Engineering the future of AI for businesses (sponsored by IBM Watson) - Ruchir Puri (IBM)
44m 58s
Why complementary learning is the future of AI (sponsored by Intel Saffron) - Bruce Horn (Intel)
38m 25s
The conversational business: Use cases and best practices for chatbots in the enterprise - Susan Etlinger (Altimeter Group)
41m 43s
The practitioner’s guide to AI - Hanlin Tang (Intel)
28m 6s
Very large-scale distributed deep learning with BigDL - Jason Dai (Intel), Ding Ding (Intel)
38m 20s
Evolving neural networks through neuroevolution - Kenneth Stanley (Uber AI Labs | University of Central Florida)
40m 26s
Ray: A distributed execution framework for reinforcement learning applications - Ion Stoica (UC Berkeley)
36m 26s
TensorFlow, machine learning, and learning to learn - Sherry Moore (Google)
38m 22s
Active learning and transfer learning - Lukas Biewald (CrowdFlower)
38m 48s
Highly dense modular acceleration clusters for deep learning - Bharadwaj Pudipeddi (NVXL Technology)
41m 36s
Reinforcement Learning Overview - Melanie Warrick (Google)
34m 38s
Deep learning on mobile: The how-to guide - Anirudh Koul (Microsoft)
45m 45s
Backing off toward simplicity: Understanding the limits of deep learning - Stephen Merity (Salesforce Research)
37m 57s
Deep reinforcement learning in the enterprise: Bridging the gap from games to industry - Mark Hammond (Bonsai)
38m 30s
Bringing gaming, VR, and AR to life with deep learning - Danny Lange (Unity Technologies)
38m 38s
Learning the learner: Using machine learning to monitor. . .machine learning? - Ira Cohen (Anodot)
37m 47s
Deep learning with limited labeled data - Avesh Singh (Cardiogram), Brandon Ballinger (Cardiogram)
30m 34s
The operating system for AI: How microservices and serverless computing enable the next generation of machine intelligence - Kenny Daniel (Algorithmia)
37m 26s
Scalable operationalization of trained CNTK and TensorFlow DNNs - Mary Wahl (Microsoft Corporation)
37m 8s
Choosing a high-performance computing development direction for original algorithms - Art Popp (ServiceNow)
40m 29s
Embedded deep learning: Deep learning for embedded systems - Siddha Ganju (Deep Vision)
38m 41s
Applications of neural-based models for conversational speech - Yishay Carmiel (IntelligentWire)
32m 35s
All the linear algebra you need for AI - Rachel Thomas (fast.ai)
36m 24s
Ensuring smarter-than-human intelligence has a positive outcome. - Nate Soares (MIRI)
51m 12s
Enabling computer-vision-based autonomous driving with affordable and reliable sensors - Shaoshan Liu (PerceptIn)
39m 20s
Why do we need new hardware for machine intelligence? - Nigel Toon (Graphcore)
34m 36s
Using deep learning toolkits with Kubernetes clusters - Wee Hyong Tok (Microsoft), Joy Qiao (Microsoft)
46m 49s
Software and hardware breakthroughs for deep neural networks at the edge - Michael B. Henry (Mythic)
36m 2s
Next-generation intelligent applications require cognitive design. - John Whalen (Brilliant Experience)
41m 23s
AI and cellular images for universal drug discovery - Blake Borgeson (Recursion Pharmaceuticals), Nan Li (Obvious Ventures)
45m 41s
Let artificial intelligence handle one of the two certainties in this world - Gang Wang (Intuit)
49m 3s
Deep learning in enterprise IoT: Use cases and challenges - Jisheng Wang (Aruba, a Hewlett Packard Enterprise Company)
37m 56s
The AI revolution’s impact on the financial services industry - Andy Steinbach (NVIDIA)
43m 41s
AI within O'Reilly Media - Paco Nathan (O'Reilly Media)
38m 38s
AI for manufacturing: Today and tomorrow - David Rogers (Sight Machine)
35m 14s
How Instacart is using AI to create the most efficient shoppers ever - Jeremy Stanley (Instacart)
34m 9s
Vertical AI: Solving full stack industry problems using subject-matter expertise, unique data, and AI to deliver a product's core value proposition - Bradford Cross (DCVC)
46m 58s
Escaping the forest, falling into the net: The winding path of Pinterest’s migration from GBDT to neural nets - Xiaofang Chen (Pinterest), Derek Cheng (Pinterest)
40m 16s
It's the organization, stupid. - Jana Eggers (Nara Logics)
39m 44s
Critical factors in building successful AI-powered conversational interfaces - Paul Tepper (Nuance Communications)
46m 3s
The potential ick factor: Ethical considerations for designing in healthcare - Astrid Chow (IBM Watson Health), Amy Chenault (Insulet), Joel Wu (Children's Minnesota)
48m 27s
Building an unbiased AI: End-to-end diversity and inclusion in AI development - Daniel Guillory (Autodesk), Matthew Scherer (Littler Mendelson, PC )
43m 21s
Incident response evolved: How AI is revolutionizing how we combat cyberthreats - Aaron Goldstein (Cylance)
33m 56s
AI for business - Jana Eggers (Nara Logics) - Part 1
52m 43s
AI for business - Jana Eggers (Nara Logics) - Part 2
51m 29s
AI for business - Jana Eggers (Nara Logics) - Part 3
31m 0s
AI for business - Jana Eggers (Nara Logics) - Part 4
35m 10s
Training vision models with public transportation datasets - Mo Patel (Think Big Analytics), Laura Froelich (Think Big Analytics, a Teradata Company) - Part 1
56m 51s
Training vision models with public transportation datasets - Mo Patel (Think Big Analytics), Laura Froelich (Think Big Analytics, a Teradata Company) - Part 2
28m 57s
Training vision models with public transportation datasets - Mo Patel (Think Big Analytics), Laura Froelich (Think Big Analytics, a Teradata Company) - Part 3
43m 33s
Training vision models with public transportation datasets - Mo Patel (Think Big Analytics), Laura Froelich (Think Big Analytics, a Teradata Company) - Part 4
28m 22s
Topological data analysis as a framework for machine intelligence - Gunnar Carlsson (Ayasdi) - Part 1
46m 50s
Topological data analysis as a framework for machine intelligence - Gunnar Carlsson (Ayasdi) - Part 2
47m 9s
Topological data analysis as a framework for machine intelligence - Gunnar Carlsson (Ayasdi) - Part 3
31m 19s
Topological data analysis as a framework for machine intelligence - Gunnar Carlsson (Ayasdi) - Part 4
41m 13s
Introduction to reinforcement learning - Marcos Campos (Bonsai) - Part 1
43m 3s
Introduction to reinforcement learning - Marcos Campos (Bonsai) - Part 2
53m 43s
Introduction to reinforcement learning - Marcos Campos (Bonsai) - Part 3
44m 18s
Introduction to reinforcement learning - Marcos Campos (Bonsai) - Part 4
50m 9s
word2vec and friends - Bruno Gonçalves (New York University) - Part 1
41m 25s
word2vec and friends - Bruno Gonçalves (New York University) - Part 2
32m 9s
word2vec and friends - Bruno Gonçalves (New York University) - Part 3
30m 27s
word2vec and friends - Bruno Gonçalves (New York University) - Part 4
48m 59s
Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 1
47m 46s
Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 2
41m 59s
Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 3
54m 46s
Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 4
39m 12s
Getting started with TensorFlow - Yufeng Guo (Google), Amy Unruh (Google) - Part 1
34m 26s
Getting started with TensorFlow - Yufeng Guo (Google), Amy Unruh (Google) - Part 2
49m 2s
Getting started with TensorFlow - Yufeng Guo (Google), Amy Unruh (Google) - Part 3
42m 3s
Getting started with TensorFlow - Yufeng Guo (Google), Amy Unruh (Google) - Part 4
46m 41s
Probabilistic programming - Vikash Mansinghka (MIT) - Part 1
32m 28s
Probabilistic programming - Vikash Mansinghka (MIT) - Part 2
52m 14s
Probabilistic programming - Vikash Mansinghka (MIT) - Part 3
48m 23s
Data science and NLP in the era of deep learning - Yinyin Liu (Intel Nervana)
40m 36s
Intel Xeon scalable processor architecture and AI workload performance - Banu Nagasundaram (Intel), Akhilesh Kumar (Intel)
32m 59s
Scalable deep learning - Ameet Talwalkar (Determined AI)
32m 56s
Intel Nervana Graph: A universal deep learning compiler - Jason Knight (Intel)
29m 32s
Accelerating deep learning - Bill Jenkins (Intel)
37m 18s
Deep learning in the enterprise: Opportunities and challenges - Ron Bodkin (Teradata)
42m 9s
Using deep learning and Google Street View to estimate the demographic makeup of the US - Timnit Gebru (Microsoft Research)
43m 25s
High-performance computing opportunities in deep learning - Greg Diamos (Baidu)
39m 11s
Self-supervised visual learning and synthesis - Alyosha Efros (UC Berkeley)
39m 10s
Conversational AI in Amazon Alexa - Ashwin Ram (Amazon)
40m 8s
A practical guide to conducting an AI snake oil sniff test - Joshua Joseph (Alpha Features)
38m 11s
Industrial robotics and deep reinforcement learning - Derik Pridmore (Osaro)
36m 33s
We're in the final stretch for—and early innings of—autonomous vehicles: Fireside chat with Shahin Farshchi and Ashu Rege - Shahin Farshchi (Lux Capital), Ashu Rege (Zoox)
38m 36s
Adversarial machine learning - Alex Kurakin (Google)
33m 35s
Scaling CNNs with Kubernetes and TensorFlow - Reza Zadeh (Stanford | Matroid)
29m 41s
A visual and intuitive understanding of deep learning - Otavio Good (Google)
39m 54s

Content preview from O'Reilly Artificial Intelligence Conference 2017 - San Francisco, CA

Training vision models with public transportation datasets - Mo Patel (Think Big Analytics), Laura Froelich (Think Big Analytics, a Teradata Company) - Part 3

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Publisher Resources

ISBN: 9781491985250

O'Reilly Artificial Intelligence Conference 2017 - San Francisco, CA

by O'Reilly Media, Inc.

Keynotes

Sponsored

Implementing AI

Interacting with AI

Verticals and applications

Impact on business and society

Tutorials

Other AI Topics

Content preview from O'Reilly Artificial Intelligence Conference 2017 - San Francisco, CA

Training vision models with public transportation datasets - Mo Patel (Think Big Analytics), Laura Froelich (Think Big Analytics, a Teradata Company) - Part 3

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

You might also like

The Artificial Intelligence Conference - San Francisco 2018

The Artificial Intelligence Conference - London, UK 2018

Strata Data Conference 2019 - San Francisco, California

Strata + Hadoop World 2017 - San Jose, California

Publisher Resources

Keynotes

Sponsored

Implementing AI

Interacting with AI

Verticals and applications

Impact on business and society

Tutorials

Other AI Topics

Content preview from O'Reilly Artificial Intelligence Conference 2017 - San Francisco, CA

Training vision models with public transportation datasets - Mo Patel (Think Big Analytics), Laura Froelich (Think Big Analytics, a Teradata Company) - Part 3

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,and much more.

You might also like

The Artificial Intelligence Conference - San Francisco 2018

The Artificial Intelligence Conference - London, UK 2018

Strata Data Conference 2019 - San Francisco, California

Strata + Hadoop World 2017 - San Jose, California

Publisher Resources

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.