Presented By O’Reilly and Intel Nervana
Put AI to work
September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

Schedule: Data science and AI sessions

9:00am–12:30pm Monday, September 18, 2017
Implementing AI
Location: Yosemite A
Average rating: ****.
(4.75, 4 ratings)
Probabilistic inference, a widely used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain or incomplete, is central to everything from big data analytics to robotics and AI. Vikash Mansinghka surveys the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to nonexperts. Read more.
9:00am–12:30pm Monday, September 18, 2017
Implementing AI
Location: Yosemite BC
Marcos Campos (Bonsai)
Average rating: **...
(2.33, 9 ratings)
Marcos Campos offers an overview of reinforcement learning, walking you through the various classes of reinforcement learning algorithms, the types of problems that can be solved with this technique, and how to build and train AI models using reinforcement learning and reward functions. Read more.
1:30pm–5:00pm Monday, September 18, 2017
Implementing AI
Location: Yosemite A
Gunnar Carlsson (Ayasdi)
Average rating: *****
(5.00, 1 rating)
Topological data analysis (TDA) is a framework for machine learning that synthesizes and combines machine learning algorithms to identify the shape of data. The technique is responsible for several major breakthroughs in our understanding of science and business. Gunnar Carlsson offers an overview of TDA's mathematical underpinnings and its practical application through software. Read more.
11:05am–11:45am Tuesday, September 19, 2017
Implementing AI
Location: Yosemite BC
Ira Cohen (Anodot)
Average rating: ****.
(4.33, 3 ratings)
The best practice in machine learning is to define a clear performance measurement for each model. However, when multiple models are deployed in parallel or feed into each other, it is infeasible to manually monitor them. Ira Cohen explains how Anodot devised a way to intelligently monitor the performance of its highly complex unsupervised machine learning models. Read more.
11:55am–12:35pm Tuesday, September 19, 2017
Implementing AI
Location: Yosemite BC
Ion Stoica (University of California, Berkeley)
Average rating: ***..
(3.75, 4 ratings)
Ion Stoica offers an overview of Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms. Read more.
1:45pm–2:25pm Tuesday, September 19, 2017
Verticals and applications
Location: Imperial A
Jeremy Stanley (Instacart)
Average rating: *****
(5.00, 2 ratings)
In the on-demand economy, if something doesn’t happen in real time, it’s too late. The secret ingredient that makes this possible? Data science. Jeremy Stanley explains how Instacart uses deep learning to enable its shoppers to become the most efficient shoppers ever, putting the company at the top of the food chart in the on-demand economy. Read more.
2:35pm–3:15pm Tuesday, September 19, 2017
Implementing AI
Location: Grand Ballroom
Kenny Daniel (Algorithmia)
Average rating: ****.
(4.00, 1 rating)
Kenny Daniel explains why AI and machine learning are a natural fit for serverless computing and shares a general architecture for scalable and serverless machine learning in production. Along the way, Kenny discusses the issues Algorithmia ran into when implementing its on-demand scaling over GPU clusters and outlines one possible vision for the future of cloud-based machine learning. Read more.
2:35pm–3:15pm Tuesday, September 19, 2017
Implementing AI
Location: Imperial B
Jason Dai (Intel), Ding Ding (Intel)
Jason Dai and Ding Ding offer an overview of BigDL, an open source distributed deep learning framework built for big data platforms. By leveraging the cluster distribution capabilities in Apache Spark, BigDL successfully unleashes the power of large-scale distributed training in deep learning, providing good performance, efficient scaling on large clusters, and good convergence results. Read more.
11:55am–12:35pm Wednesday, September 20, 2017
Verticals and applications
Location: Yosemite A
David Rogers (Sight Machine)
Average rating: ***..
(3.33, 3 ratings)
Artificial intelligence in manufacturing has been around for a long time, but are you aware of how it can make your operations more efficient and profitable? David Rogers explains how existing technologies like the digital twin approach, advanced decision making, and downtime cause detection have primed manufacturing for a profitable and efficient future. Read more.
11:55am–12:35pm Wednesday, September 20, 2017
Impact on business and society
Location: Franciscan AB
Tim Estes (Digital Reasoning)
As AI moves from concept to reality, debates about ethics are evolving into excitement and the desire to learn more about AI and its promise of a better world. Tim Estes discusses two customer use cases at Digital Reasoning: Nasdaq, which found a way to use AI to help safeguard financial markets, and Thorn, which found a way to use AI to combat human trafficking and rescue children. Read more.
1:45pm–2:25pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial A
Average rating: ****.
(4.00, 1 rating)
Current driving policy models are limited to models trained using homogenous data from a small number of vehicles running in controlled environments. Bruno Fernandez-Ruiz offers an overview of a network of connected devices that is building an end-to-end driving policy to leverage the 10 trillion miles driven every year. Read more.
4:00pm–4:40pm Wednesday, September 20, 2017
Implementing AI
Location: Grand Ballroom
Wee Hyong Tok (Microsoft), Joy Qiao (Microsoft)
Average rating: *****
(5.00, 1 rating)
Joy Qiao and Wee Hyong Tok demonstrate how to combine Kubernetes clusters and deep learning toolkits to get the best of both worlds and jumpstart the development of innovative deep learning applications. Along the way, Joy and Wee Hyong explain how to train deep neural networks using GPU-enabled containers orchestrated by Kubernetes with common deep learning toolkits, such as CNTK and TensorFlow. Read more.
4:00pm–4:40pm Wednesday, September 20, 2017
Implementing AI
Location: Yosemite BC
Melanie Warrick (Google)
Average rating: ****.
(4.00, 4 ratings)
Reinforcement learning is a popular subfield in machine learning because of its success in beating humans at complex games like Go and Atari. The field’s value is in utilizing an award system to develop models and find more optimal ways to solve complex, real-world problems. This approach allows software to adapt to its environment without full knowledge of what the results should look like. Read more.
4:50pm–5:30pm Wednesday, September 20, 2017
Impact on business and society
Location: Grand Ballroom
Nikita Lytkin (Facebook)
Average rating: ****.
(4.50, 2 ratings)
Nikita Lytkin explains how Facebook uses machine learning technologies developed by its ads ranking, applied machine learning, and AI research teams to enable personalized ecommerce that recommends a vast diversity of products to nearly two billion people. Read more.