"Putting AI to Work" was the theme of AI San Francisco 2017 and this sold out conference delivered on that theme with more than 100 of the world's top AI researchers, engineers, data scientists, and venture capitalists presenting real-world implementations of AI in medicine, autonomous vehicles, smart phones, voice-based personal assistants, in-store shopping, financial services, media, IoT, and more. …
"Putting AI to Work" was the theme of AI San Francisco 2017 and this sold out conference delivered on that theme with more than 100 of the world's top AI researchers, engineers, data scientists, and venture capitalists presenting real-world implementations of AI in medicine, autonomous vehicles, smart phones, voice-based personal assistants, in-store shopping, financial services, media, IoT, and more.
This video compilation gives you complete access to all of the conference's 14 keynotes, 10 tutorials, and 70 plus sessions. You'll hear keynotes from Andrew Ng (Coursera) on how AI will revolutionize the world just as electricity did 100 years ago; Jia Li (Google Cloud) on why a democratized approach to AI will bestow AI's benefits to the widest audience possible; Tim O'Reilly (O'Reilly Media) on the AI design choices we must make to avoid a world ruled by hostile AI machines; and you'll get to listen to what the VC's think about AI in a keynote by Vijay Pande (Andreessen Horowitz), and a fireside chat between Steve Jurvetson (DFJ Venture Capital) and Naveen Rao (Intel).
Revealing AI's important new tools and frameworks was a primary objective of AI SF 2017. You'll be able to learn about all of these advances in sessions, such as Jason Knight (Intel) on Intel's Nervana Graph project (a universal deep learning compiler); Ion Stoica's (UC Berkeley) on Ray, a new distributed execution framework for reinforcement learning applications; Mary Wahl (Microsoft) on scalable operationalization of trained CNTK and TensorFlow DNNs; and Jeremy Howard (fast.ai) on using GPU acceleration with PyTorch to make your algorithms 2,000% faster.
But the real foci of the conference were the AI implementation sessions and this compilation lets you listen in on all of them. You'll learn how AI is working in medicine, with presentations on how AI uses cellular images to discover new drugs; how AI applies to healthcare's biggest opportunity—clinical variation; and how AI is helping cure cancer. From AI researchers in the transportation sector, you'll hear how affordable and reliable sensors enable computer-vision-based autonomous driving and how to train vision models for object detection. And in the world of consumer products, you'll get an overview of how Instacart uses deep learning to optimize the in-store shopping experience; an update on conversational AI in Amazon's Alexa; a preview of Intuit's AI driven self-filing tax system; a look at how deep learning is producing transformative experiences in gaming and VR; and multiple sessions on AI implementations on smart phones.
Keynotes
The inevitable merger of IQ and EQ in technology - Rana el Kaliouby (Affectiva)
Engineering the future of AI for businesses (sponsored by IBM Watson) - Ruchir Puri (IBM)
The state of AI adoption - Ben Lorica (O’Reilly Media), Roger Chen (.)
Deep learning to fight cancer: Fireside chat - Peter Norvig (Google), Abu Qader (GliaLab)
AI is the new electricity. - Andrew Ng (Coursera)
How to escape saddle points efficiently - Michael Jordan (UC Berkeley)
Why democratizing AI matters: Computing, data, algorithms, and talent - Jia Li (Google)
AI mimicking nature: Flying and talking (sponsored by Microsoft) - Lili Cheng (Microsoft)
Accelerating AI - Steve Jurvetson (DFJ)
Fireside chat with Naveen Rao and Steve Jurvetson - Naveen Rao (Intel), Steve Jurvetson (DFJ)
Build smart applications with your new super power: Cloud AI (sponsored by Google Cloud) - Philippe Poutonnet (Google)
Our Skynet moment - Tim O’Reilly (O’Reilly Media)
Sponsored
Scalable deep learning with Microsoft Cognitive Toolkit (sponsored by Microsoft) - Anusua Trivedi (Microsoft)
Pushing the boundaries of ML using TensorFlow and Google Cloud (sponsored by Google Cloud) - Magnus Hyttsten (Google)
Engineering the future of AI for businesses (sponsored by IBM Watson) - Ruchir Puri (IBM)
Why complementary learning is the future of AI (sponsored by Intel Saffron) - Bruce Horn (Intel)
Implementing AI
The conversational business: Use cases and best practices for chatbots in the enterprise - Susan Etlinger (Altimeter Group)
The practitioner’s guide to AI - Hanlin Tang (Intel)
Very large-scale distributed deep learning with BigDL - Jason Dai (Intel), Ding Ding (Intel)
Evolving neural networks through neuroevolution - Kenneth Stanley (Uber AI Labs | University of Central Florida)
Ray: A distributed execution framework for reinforcement learning applications - Ion Stoica (UC Berkeley)
TensorFlow, machine learning, and learning to learn - Sherry Moore (Google)
Active learning and transfer learning - Lukas Biewald (CrowdFlower)
Highly dense modular acceleration clusters for deep learning - Bharadwaj Pudipeddi (NVXL Technology)
Reinforcement Learning Overview - Melanie Warrick (Google)
Deep learning on mobile: The how-to guide - Anirudh Koul (Microsoft)
Backing off toward simplicity: Understanding the limits of deep learning - Stephen Merity (Salesforce Research)
Deep reinforcement learning in the enterprise: Bridging the gap from games to industry - Mark Hammond (Bonsai)
Bringing gaming, VR, and AR to life with deep learning - Danny Lange (Unity Technologies)
Learning the learner: Using machine learning to monitor. . .machine learning? - Ira Cohen (Anodot)
Deep learning with limited labeled data - Avesh Singh (Cardiogram), Brandon Ballinger (Cardiogram)
The operating system for AI: How microservices and serverless computing enable the next generation of machine intelligence - Kenny Daniel (Algorithmia)
Scalable operationalization of trained CNTK and TensorFlow DNNs - Mary Wahl (Microsoft Corporation)
Choosing a high-performance computing development direction for original algorithms - Art Popp (ServiceNow)
Embedded deep learning: Deep learning for embedded systems - Siddha Ganju (Deep Vision)
Applications of neural-based models for conversational speech - Yishay Carmiel (IntelligentWire)
All the linear algebra you need for AI - Rachel Thomas (fast.ai)
Ensuring smarter-than-human intelligence has a positive outcome. - Nate Soares (MIRI)
Enabling computer-vision-based autonomous driving with affordable and reliable sensors - Shaoshan Liu (PerceptIn)
Why do we need new hardware for machine intelligence? - Nigel Toon (Graphcore)
Using deep learning toolkits with Kubernetes clusters - Wee Hyong Tok (Microsoft), Joy Qiao (Microsoft)
Interacting with AI
Software and hardware breakthroughs for deep neural networks at the edge - Michael B. Henry (Mythic)
Next-generation intelligent applications require cognitive design. - John Whalen (Brilliant Experience)
Verticals and applications
AI and cellular images for universal drug discovery - Blake Borgeson (Recursion Pharmaceuticals), Nan Li (Obvious Ventures)
Let artificial intelligence handle one of the two certainties in this world - Gang Wang (Intuit)
Deep learning in enterprise IoT: Use cases and challenges - Jisheng Wang (Aruba, a Hewlett Packard Enterprise Company)
The AI revolution’s impact on the financial services industry - Andy Steinbach (NVIDIA)
AI within O’Reilly Media - Paco Nathan (O’Reilly Media)
AI for manufacturing: Today and tomorrow - David Rogers (Sight Machine)
How Instacart is using AI to create the most efficient shoppers ever - Jeremy Stanley (Instacart)
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)
Impact on business and society
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)
It’s the organization, stupid. - Jana Eggers (Nara Logics)
Critical factors in building successful AI-powered conversational interfaces - Paul Tepper (Nuance Communications)
The potential ick factor: Ethical considerations for designing in healthcare - Astrid Chow (IBM Watson Health), Amy Chenault (Insulet), Joel Wu (Children’s Minnesota)
Building an unbiased AI: End-to-end diversity and inclusion in AI development - Daniel Guillory (Autodesk), Matthew Scherer (Littler Mendelson, PC )
Incident response evolved: How AI is revolutionizing how we combat cyberthreats - Aaron Goldstein (Cylance)
Tutorials
AI for business - Jana Eggers (Nara Logics) - Part 1
AI for business - Jana Eggers (Nara Logics) - Part 2
AI for business - Jana Eggers (Nara Logics) - Part 3
AI for business - Jana Eggers (Nara Logics) - Part 4
Training vision models with public transportation datasets - Mo Patel (Think Big Analytics), Laura Froelich (Think Big Analytics, a Teradata Company) - Part 1
Training vision models with public transportation datasets - Mo Patel (Think Big Analytics), Laura Froelich (Think Big Analytics, a Teradata Company) - Part 2
Training vision models with public transportation datasets - Mo Patel (Think Big Analytics), Laura Froelich (Think Big Analytics, a Teradata Company) - Part 3
Training vision models with public transportation datasets - Mo Patel (Think Big Analytics), Laura Froelich (Think Big Analytics, a Teradata Company) - Part 4
Topological data analysis as a framework for machine intelligence - Gunnar Carlsson (Ayasdi) - Part 1
Topological data analysis as a framework for machine intelligence - Gunnar Carlsson (Ayasdi) - Part 2
Topological data analysis as a framework for machine intelligence - Gunnar Carlsson (Ayasdi) - Part 3
Topological data analysis as a framework for machine intelligence - Gunnar Carlsson (Ayasdi) - Part 4
Introduction to reinforcement learning - Marcos Campos (Bonsai) - Part 1
Introduction to reinforcement learning - Marcos Campos (Bonsai) - Part 2
Introduction to reinforcement learning - Marcos Campos (Bonsai) - Part 3
Introduction to reinforcement learning - Marcos Campos (Bonsai) - Part 4
word2vec and friends - Bruno Gonçalves (New York University) - Part 1
word2vec and friends - Bruno Gonçalves (New York University) - Part 2
word2vec and friends - Bruno Gonçalves (New York University) - Part 3
word2vec and friends - Bruno Gonçalves (New York University) - Part 4
Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 1
Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 2
Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 3
Here and now: Bringing AI into the enterprise - Kristian Hammond (Narrative Science) - Part 4
Getting started with TensorFlow - Yufeng Guo (Google), Amy Unruh (Google) - Part 1
Getting started with TensorFlow - Yufeng Guo (Google), Amy Unruh (Google) - Part 2
Getting started with TensorFlow - Yufeng Guo (Google), Amy Unruh (Google) - Part 3
Getting started with TensorFlow - Yufeng Guo (Google), Amy Unruh (Google) - Part 4
Probabilistic programming - Vikash Mansinghka (MIT) - Part 1
Probabilistic programming - Vikash Mansinghka (MIT) - Part 2
Probabilistic programming - Vikash Mansinghka (MIT) - Part 3
Other AI Topics
Data science and NLP in the era of deep learning - Yinyin Liu (Intel Nervana)
Intel Xeon scalable processor architecture and AI workload performance - Banu Nagasundaram (Intel), Akhilesh Kumar (Intel)
Scalable deep learning - Ameet Talwalkar (Determined AI)
Intel Nervana Graph: A universal deep learning compiler - Jason Knight (Intel)
Accelerating deep learning - Bill Jenkins (Intel)
Deep learning in the enterprise: Opportunities and challenges - Ron Bodkin (Teradata)
Using deep learning and Google Street View to estimate the demographic makeup of the US - Timnit Gebru (Microsoft Research)
High-performance computing opportunities in deep learning - Greg Diamos (Baidu)
Self-supervised visual learning and synthesis - Alyosha Efros (UC Berkeley)
Conversational AI in Amazon Alexa - Ashwin Ram (Amazon)
A practical guide to conducting an AI snake oil sniff test - Joshua Joseph (Alpha Features)
Industrial robotics and deep reinforcement learning - Derik Pridmore (Osaro)
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)
Adversarial machine learning - Alex Kurakin (Google)
Scaling CNNs with Kubernetes and TensorFlow - Reza Zadeh (Stanford | Matroid)
A visual and intuitive understanding of deep learning - Otavio Good (Google)