The Artificial Intelligence Conference SF 2018 was all about putting AI to work right now, giving conference attendees the opportunity to cut through the noise to see what's real and what's coming soon in the world of applied AI. Want to hear what Andrew Feldman (Cerebras Systems) can share about the next generation of machine learning hardware? What Ting-Fang Yen (DataVisor) reveals about the ways her company …
The Artificial Intelligence Conference SF 2018 was all about putting AI to work right now, giving conference attendees the opportunity to cut through the noise to see what's real and what's coming soon in the world of applied AI. Want to hear what Andrew Feldman (Cerebras Systems) can share about the next generation of machine learning hardware? What Ting-Fang Yen (DataVisor) reveals about the ways her company creates real-time, scalable fraud detection solutions using deep learning, Spark and TensorFlow? What Sarah Bird (Facebook) can teach us about the Amazon-Microsoft-Facebook backed Open Neural Network Exchange (ONNX) and why this framework makes it possible to rapidly move AI projects from research to reality? This video compilation gives you the chance to learn about these subjects and much, much more. It's a complete video recording of the 15 keynotes, 11 tutorials, and 100+ technical sessions delivered at AI SF 2018 by the world's top AI strategists, developers, researchers, and data scientists.
Highlights include:
Keynotes
Beyond Hype – AI in the Real World - Julie Shin Choi (Intel AI)
Using machine learning in workload automation (sponsored by Digitate) - Akhilesh Tripathi (Digitate)
Unlocking innovation in AI - Ben Lorica (O’Reilly Media), Roger Chen (Computable Labs)
AI Foundations: What shapes the AI that’s shaping our world? - Meredith Whittaker (AI Now Institute, NYU)
AI at Scale at Coinbase (sponsored by Amazon Web Services) - Soups Ranjan (Coinbase)
OpenAI and the path towards safe AGI - Greg Brockman (OpenAI)
Raising AI to benefit business and society (sponsored by Accenture) - Kishore Durg (Accenture)
China: AI superpower - Kai-Fu Lee (Sinovation Ventures)
Fireside chat with Tim O’Reilly and Kai-Fu Lee - Kai-Fu Lee (Sinovation Ventures), Tim O’Reilly (O’Reilly Media)
Machine Learning In The Cloud - Hagay Lupesko (Amazon Web Services)
Customized ML for the enterprise (sponsored by Google) - Levent Besik (Google)
Accelerating AI on Xeon through SW optimization - Huma Abidi (Intel)
AI and security: Lessons, challenges, and future directions - Dawn Song (UC Berkeley)
Four success factors for building your AI business journey (sponsored by IBM Watson) - Manish Goyal (IBM Watson)
The breadth of AI applications: The ongoing expansion - Peter Norvig (Google)
Connected Arms (sponsored by Microsoft) - Joseph Sirosh (Microsoft)
A new golden age for computer architecture - David Patterson (UC Berkeley)
Implementing AI
Reinforcement learning for mixed autonomy mobility - Cathy Wu (UC Berkeley)
Accelerating research to production with PyTorch 1.0 - Joseph Spisak (Facebook)
AI canonical architecture and cybersecurity examples - David Martinez (MIT Lincoln Laboratory)
How Captricity built a human-level handwriting recognition engine using data-driven AI - Ramesh Sridharan (Captricity)
The future of AI is distributed: Peer-to-peer learning and multi-agent AI at the edge - Noah Schwartz (Quorum AI)
Distributed TensorFlow training using Keras and Kubernetes - Magnus Hyttsten (Google), Priya Gupta (Google)
Explaining machine learning models - Armen Donigian (ZestFinance)
Data privacy and its implication to deep learning - Yishay Carmiel (IntelligentWire)
Evaluate deep Q-learning for sequential targeted marketing with 10-fold cross-validation - Jian Wu (NIO)
High-performance input pipelines for scalable deep learning - Vas Chellappa (Pure Storage)
Edge intelligence: Machine learning at the enterprise edge - Simon Crosby (SWIM Inc.)
Do-it-yourself artificial intelligence - Alasdair Allan (Babilim Light Industries)
Enabling affordable but reliable autonomous driving - Shaoshan Liu (PerceptIn)
Slaying the beasts of scalability and explainability - Alex Wong (DarwinAI)
Productionalizing deep learning for computer vision - Labhesh Patel (Jumio)
Portability and performance in embedded deep learning: Can we have both? - Cormac Brick (Intel)
Reinforcement learning and the future of software - Danny Goodman (Switchback Ventures)
A data-driven approach to building AI-powered bots and virtual agents - Ofer Ronen (Chatbase)
Practical issues in building and deploying deep learning models - Lukas Biewald (Weights Biases)
Blockchain and AI Summit
Why decentralized technology matters - Alex Gladstein (Human Rights Foundation)
Blockchain Is Changing Everyday Life - Jerry Cuomo (IBM)
Decentralized data markets in theory and practice - Bharath Ramsundar (Computable)
The Role of a Decentralized Data Marketplace in the Future of AI - Caroline Sofiatti (Computable Labs )
Sharing clinical data on the blockchain - Robert Currie (UCSC Genomics Institute)
Turning the “fat protocol” on its side: Making the case for simple distributed ledgers Valentin Bercovici (PencilDATA)
Open source decentralized data markets for training AI in areas of large shared risk - Paco Nathan (derwen.ai)
Sponsored
Create customer value with Google Cloud AI (sponsored by Google Cloud) - Anand Iyer (Google)
Removing complexity for workload automation with machine learning (sponsored by Digitate) - Jayanti Murty (Digitate)
Teach and test your AI systems (sponsored by Accenture) - Kishore Durg (Accenture), Teresa Escrig (Accenture)
A next-generation NVMe-native parallel filesystem for accelerating AI workloads (sponsored by WekaIO) - Liran Zvibel (WekaIO)
Tensor2Tensor (sponsored by Google) - Lucasz Kaiser (Google)
Frontiers of TensorFlow: Space, statistics, and probabilistic ML (sponsored by Google) - Josh Dillon (Google), Wahid Bhimji (NERSC)
AutoGraph and Cloud TPUs (sponsored by Google) - Alexandre Passos (Google), Frank Chen (Google )
Ready, set, go: Using TensorFlow to prototype, train, and productionalize your models (sponsored by Google) - Karmel Allison (Google)
Cloud AutoML: Customize machine learning models with your own data (sponsored by Google) - Torry Yang (Google)
TensorFlow Extended: An end-to-end machine learning platform for TensorFlow (sponsored by Google Cloud) - Clemens Mewald (Google)
Swift for TensorFlow and TensorFlow Lite (sponsored by Google Cloud) - Richard Wei (Google), Andrew Selle (Google)
TensorFlow for JavaScript (sponsored by Google Cloud) - Nick Kreeger (Google), Ping Yu (Google)
TensorFlow: Roadmap and community (sponsored by Google Cloud) - Laurence Moroney (Google), Edd Wilder-James (Google), Sandeep Gupta (Google)
TensorFlow: Machine learning for programmers (sponsored by Google Cloud) - Laurence Moroney (Google)
End-to-end machine learning with TensorFlow on GCP Day (sponsored by Google Cloud) Part 1
End-to-end machine learning with TensorFlow on GCP Day (sponsored by Google Cloud) Part 2
End-to-end machine learning with TensorFlow on GCP Day (sponsored by Google Cloud) Part 3
End-to-end machine learning with TensorFlow on GCP Day (sponsored by Google Cloud) Part 4
End-to-end machine learning with TensorFlow on GCP Day (sponsored by Google Cloud) Part 5
End-to-end machine learning with TensorFlow on GCP Day (sponsored by Google Cloud) Part 6
AI/CUSTOMERS/IDEAS: Customer Feedback Management Using Next Generation AI (sponsored by Gamalon) - Benjamin Vigoda (Gamalon)
Distributed deep domain adaptation for automated poacher detection (sponsored by Microsoft) - Mark Hamilton (Microsoft)
Building and deploying AI: A modern platform for the enterprise (sponsored by SAS) - Alex Ge (SAS)
Framing business problems as ML problems (sponsored by AWS) - Carlos Escapa (AWS)
From ingest to predict: Building an effective ML pipeline (sponsored by AWS) - Ujjwal Ratan (AWS)
Less Firefighting, More Strategizing: Lessons Learned from Implementing AI for TechOps (sponsored by TelescopeAI by EPAM) - Jitin Agarwal (EPAM Systems)
Achieving transformative business outcomes with artificial intelligence (sponsored by Teradata) - Ranjeeta Singh (Teradata), Nick Switanek (Teradata)
Making AI a Killer App for your Data: A Practical Guide (sponsored by IBM Watson) - Manish Goyal (IBM)
Models and Methods
Improving customer support with natural language processing and deep learning - Piero Molino (Uber)
A novel adoption of LSTM in customer touchpoint prediction problems - KC Tung (AT)
Lung cancer detection and segmentation using deep learning - Daniel Golden (Arterys)
Artificial intelligence open source libraries - Sarah Bird (Facebook)
Hit a home run making baseball decisions using artificial intelligence and machine learning - David Kearns (IBM), Ari Kaplan (Aginity), Erin Ledell (H2O.ai), Christopher Coad (Aginity)
Trustless machine learning contracts: Evaluating and exchanging machine learning models on the Ethereum blockchain - A. Besir Kurtulmus (Algorithmia)
Decentralized data markets for training AI models - Roger Chen (Computable Labs)
Making machine learning easy with embeddings - Abhishek Tayal (Twitter)
Leaving no one behind: Make equal access to teamwork possible with deep learning-enabled sign language and gesture recognition - Goodman Gu (Cogito)
Predicting short-term driving intention using recurrent neural network on sequential data - Zhou Xing (Borgward R Silicon Valley)
Delayed impact of fair machine learning - Lydia T. Liu (UC Berkeley)
Deep reinforcement and meta-learning: Building flexible and adaptable machine intelligence - Sergey Levine (UC Berkeley)
AI Business Summit
Building machines that can read and write - Sean Gourley (Primer)
Executive Briefing: What you must know to build AI systems that understand natural language - David Talby (Pacific AI)
Applications of AI for quantitative finance at Thomson Reuters - Joe Rothermich (Thomson Reuters Labs)
Data-driven healthcare - Shelley Zhuang (11.2 Capital)
Inside Out: The Impact of AI Data on Transforming the Enterprise - Rudina Seseri (Glasswing Ventures), Brian Eberman (Independent), Rob May (Talla)
Executive Briefing: Is there a Moore’s law for artificial intelligence? - Benjamin Vigoda (Gamalon)
AI for Earth: Using machine learning to monitor, model, and manage natural resources - Jennifer Marsman (Microsoft)
Trustworthiness of machine learning applications - Mayukh Bhaowal (Salesforce)
AI for improving teaching and learning - Varun Arora (Baidu USA)
Fighting human trafficking with AI - Mayank Kejriwal (USC Information Sciences Institute)
Deep Learning models
How to use transfer learning to bootstrap image classification and question answering (QA) - Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft)
Deep learning on mobile: The how-to guide - Anirudh Koul (Microsoft)
Deep learning for time series data - Ira Cohen (Anodot), Arun Kejariwal (Independent)
Deep Learning in Practice - Evan Sparks (Determined AI)
Debuggable deep learning - Avesh Singh (Cardiogram), Kevin Wu (Cardiogram)
Predicting Alzheimer’s: Generating neural networks to detect the neurodegenerative disease - Ayin Vala (DeepMD | Foundation for Precision Medicine)
Evolutionary computation: The next deep learning - Risto Miikkulainen (Sentient.ai)
Reverse engineering your AI prototype and the road to reproducibility - Brian D’alessandro (Zocdoc), Chris Smith (Zocdoc)
Neural Network Distiller: A PyTorch environment for neural network compression - Neta Zmora (Intel AI Lab)
Deep reinforcement learning for robotics - Woj Zaremba (OpenAI)
Achieving personalization with LSTMs - Ankit Jain (Uber)
Accelerating Deep Learning inference using OpenVino across Intel Platforms - Dmitry Rizshkov (Intel)
Deep learning for large-scale online fraud detection - Ting-Fang Yen (DataVisor)
Impact of AI on Business and Society
Design thinking for AI - Chris Butler (Philosophie) Part 1
Design thinking for AI - Chris Butler (Philosophie) Part 2
Design thinking for AI - Chris Butler (Philosophie) Part 3
Design thinking for AI - Chris Butler (Philosophie) Part 4
The wiring diagram of arXiv’s AI papers - Jana Eggers (Nara Logics)
On the road to artificial general intelligence - Danny Lange (Unity Technologies)
Forming meaningful relationships between human and machine - Adam Cutler (IBM Design)
Executive Briefing: When privacy scales—Intelligent product design under global data privacy regulation - Amanda Casari (SAP Concur)
How Autodesk is humanizing customer support with AI: Meet AVA - Rachael Rekart (Autodesk )
Executive Briefing: AI safety—Problems, state of the art, and alternatives - Allison Duettmann (Foresight Institute)
Human-machine teaming: Why the human element will always be indispensable in cybersecurity - Candace Worley (McAfee)
Executive Briefing: Ethical AI—How to build products that customers will love and trust - Susan Etlinger (Altimeter Group)
Executive Briefing: Organizational design for effective AI - Mariya Yao (Metamaven)
Learning from video games - Paris Buttfield-Addison (Secret Lab Pty. Ltd.), Tim Nugent (lonely.coffee), Mars Geldard (University of Tasmania)
AI: A force for good - Jake Porway (DataKind)
AI for Good - Aleksandra Mojsilovic (IBM)
Q with Kai Fu Lee - Kai-Fu Lee (Sinovation Ventures)
AI in the Enterprise
Executive Briefing: Moving AI off your product roadmap and into your products - Ashok Srivastava (Intuit)
Executive Briefing: How to develop a full stack deep learning team - Forrest Iandola (DeepScale)
Speed versus specificity: Designing text annotation tasks for the people and algorithms that drive human-in-the-loop (HIL) products - Jason Laska (Clara Labs)
AI is my copilot. - Jake Saper (Emergence Capital)
Executive Briefing: Knowledge graphs for AI - Mike Tung (Diffbot)
Executive Briefing: A multichannel chatbot strategy - Sharad Gupta (Blue Shield of California)
Executive Briefing: Best practices for human in the loop—The business case for active learning - Paco Nathan (derwen.ai)
Your deep learning applications want scale (and how you can support them) - Joel Hestness (Baidu)
Machine learning in investment management - Michael Weinberg (Mov37)
Trends in AI systems - Casimir Wierzynski (Intel AI)
AI demand is highly elastic: How cost-effective AI inference hardware will open massive markets - Michael B. Henry (Mythic)
Machine learning for optimizing construction - Ramzi Roy Labban (Consolidated Contractors Company (CCC))
Tutorials
Building reinforcement learning applications with Ray - Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley), Ion Stoica (UC Berkeley)
Taming dragons: A breakthrough approach to AI for business leaders - Beth Partridge (milk+honey), Nick Paquin (milk+honey), Annie O’Connor (milk+honey) Part 1
Taming dragons: A breakthrough approach to AI for business leaders - Beth Partridge (milk+honey), Nick Paquin (milk+honey), Annie O’Connor (milk+honey) Part 2
Taming dragons: A breakthrough approach to AI for business leaders - Beth Partridge (milk+honey), Nick Paquin (milk+honey), Annie O’Connor (milk+honey) Part 3
PyTorch: A flexible approach for computer vision models - Mo Patel (Independent), David Mueller (Teradata) - Part 1
PyTorch: A flexible approach for computer vision models - Mo Patel (Independent), David Mueller (Teradata) - Part 2
PyTorch: A flexible approach for computer vision models - Mo Patel (Independent), David Mueller (Teradata) - Part 3
PyTorch: A flexible approach for computer vision models - Mo Patel (Independent), David Mueller (Teradata) - Part 4
Building deep learning applications with Amazon SageMaker - David Arpin (Amazon Web Services) Part 1
Building deep learning applications with Amazon SageMaker - David Arpin (Amazon Web Services) Part 2
Building deep learning applications with Amazon SageMaker - David Arpin (Amazon Web Services) Part 3
AI on Kubernetes - Daniel Whitenack (Pachyderm) Part 1
AI on Kubernetes - Daniel Whitenack (Pachyderm) Part 2
AI on Kubernetes - Daniel Whitenack (Pachyderm) Part 3
AI on Kubernetes - Daniel Whitenack (Pachyderm) Part 4
Building intelligent mobile applications in healthcare - Xiaoyong Zhu (Microsoft), Wilson Lee (Microsoft), Ivan Tarapov (Microsoft), Mazen Zawaideh (University of Washington Medical Center) Part 1
Building intelligent mobile applications in healthcare - Xiaoyong Zhu (Microsoft), Wilson Lee (Microsoft), Ivan Tarapov (Microsoft), Mazen Zawaideh (University of Washington Medical Center) Part 2
Building intelligent mobile applications in healthcare - Xiaoyong Zhu (Microsoft), Wilson Lee (Microsoft), Ivan Tarapov (Microsoft), Mazen Zawaideh (University of Washington Medical Center) Part 3
Distributed deep learning in the cloud: Build an end-to-end application involving computer vision and geospatial data - Mary Wahl (Microsoft), Banibrata De (Microsoft) Part 1
Distributed deep learning in the cloud: Build an end-to-end application involving computer vision and geospatial data - Mary Wahl (Microsoft), Banibrata De (Microsoft) Part 2
Distributed deep learning in the cloud: Build an end-to-end application involving computer vision and geospatial data - Mary Wahl (Microsoft), Banibrata De (Microsoft) Part 3
Distributed deep learning in the cloud: Build an end-to-end application involving computer vision and geospatial data - Mary Wahl (Microsoft), Banibrata De (Microsoft) Part 4