Presented By O’Reilly and Intel AI
Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

Schedule: Edge computing and Hardware sessions

1:30pm-5:00pm Wednesday, September 5, 2018
Xiaoyong Zhu (Microsoft), Wilson Lee (CLOUD AI) (Microsoft), Ivan Tarapov (Microsoft), Mazen Zawaideh (University of Washington Medical Center)
Xiaoyong Zhu, Gheorghe Iordanescu, Wilson Lee, and Ivan Tarapov walk you through building a deep learning model and intelligent applications on edge devices running iOS, Android, and Windows, using a working example that helps clinicians in areas with less access to radiologists identify possible lung diseases. Read more.
9:10am-9:20am Thursday, September 6, 2018
Location: Continental Ballroom 4-6
Ben Lorica (O'Reilly Media), Roger Chen (Computable)
Average rating: ***..
(3.50, 4 ratings)
Ben Lorica and Roger Chen describe the state of adoption of AI technologies and provide a glimpse into tools and trends that are poised to accelerate innovation and the introduction of new applications. Read more.
11:05am-11:45am Thursday, September 6, 2018
Implementing AI
Location: Imperial B
Simon Crosby (SWIM Inc.)
Simon Crosby details an architecture for learning on time series data using edge devices, based on the distributed actor model. This approach flies in the face of the traditional wisdom of cloud-based, big-data solutions to ML problems. You'll see that there are more than enough resources at “the edge” to cost-effectively analyze, learn from, and predict from streaming data on the fly. Read more.
11:05am-11:45am Thursday, September 6, 2018
Location: Continental 7-9
Neta Zmora (Intel AI Lab)
Neta Zmora offers an overview of Distiller, an open source Python package for neural network compression research. Neta discusses the motivation for compressing DNNs, outlines compression approaches, and explores Distiller's design and tools, supported algorithms, and code and documentation. Neta concludes with an example implementation of a compression research paper. Read more.
11:55am-12:35pm Thursday, September 6, 2018
Anirudh Koul (Microsoft)
Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially for computer vision. Anirudh Koul explains how to bring the power of convolutional neural networks and deep learning to memory- and power-constrained devices like smartphones. Read more.
11:55am-12:35pm Thursday, September 6, 2018
Sponsored
Location: Franciscan BCD
Liran Zvibel (WekaIO)
Artificial intelligence requires a low-latency, high-throughput storage system to keep the compute layer fully saturated with data. Liran Zvibel demonstrates why NVMe-optimized, distributed filesystems are ideal storage solutions to support AI applications and introduces a next-gen massively parallel shared filesystem that's NAND flash and NVMe optimized, built to solve the I/O starvation problem. Read more.
1:45pm-2:25pm Thursday, September 6, 2018
Implementing AI
Location: Imperial B
Shaoshan Liu (PerceptIn)
Average rating: ***..
(3.00, 1 rating)
Shaoshan Liu explains how PerceptIn built a reliable autonomous vehicle with a total cost under $10,000. Read more.
1:45pm-2:25pm Thursday, September 6, 2018
Implementing AI, Models and Methods
Location: Imperial A
Evan Sparks (Determined AI), Ameet Talwalkar (Carnegie Mellon University | Determined AI)
Average rating: *****
(5.00, 1 rating)
In spite of the enormous excitement about the potential of deep learning, several key challenges—from prohibitive hardware requirements to immature software offerings—are impeding its widespread enterprise adoption. Evan Sparks and Ameet Talwalkar detail fundamental challenges facing organizations looking to adopt deep learning and present novel solutions to overcome several of them. Read more.
4:50pm-5:30pm Thursday, September 6, 2018
David Kearns (IBM), Ari Kaplan (Aginity), Erin Ledell (H2O.ai), Christopher Coad (Aginity)
Average rating: *****
(5.00, 2 ratings)
Join Ari Kaplan, Erin LeDell, Chris Coad, and David Kearns to see where AI meets business intelligence, as they explore the latest ML technologies and concepts powering today's decisions, including Hortonworks, Aginity Amp, H2O.ai, IBM Data Science Experience, and more—using real-life baseball data to illustrate the concepts. Read more.
10:10am-10:30am Friday, September 7, 2018
Location: Continental Ballroom 4-6
David Patterson (UC Berkeley)
Average rating: *****
(5.00, 1 rating)
High-level, domain-specific languages and architectures and freeing architects from the chains of proprietary instruction sets will usher in a new golden age. David Patterson explains why, despite the end of Moore’s law, he expects an outpouring of codesigned ML-specific chips and supercomputers that will improve even faster than Moore’s original 1965 prediction. Read more.
11:05am-11:45am Friday, September 7, 2018
Location: Imperial B
Michael B. Henry (Mythic)
As prices drop, new markets open up. AI inference will likely follow the same trends as general purpose compute or storage, and the market for AI hardware and software stacks could approach $100B in the next 10 years. Michael Henry dives into AI innovation at a hardware and software level. Read more.
11:55am-12:35pm Friday, September 7, 2018
Location: Continental 7-9
Cormac Brick (Intel)
In recent years, there has been lots of work done on low-precision inference that shows that by training for quantization, large gains in energy efficiency can be achieved. Cormac Brick offers a look at industry challenges and progress needed to close the portability performance gap. Read more.
11:55am-12:35pm Friday, September 7, 2018
Implementing AI
Location: Imperial B
Andrew Feldman (Cerebras Systems)
Session by Andrew Feldman Read more.
1:45pm-2:25pm Friday, September 7, 2018
Implementing AI
Location: Imperial B
Neil Tan (ARM)
Would you believe that AI inferencing can be done on chips that cost less than a dollar? uTensor, a custom TensorFlow runtime for microcontrollers (MCUs), lets you do just that. Neil Tan offers an overview of uTensor, the first framework to streamline model deployments on MCUs, allowing you to push AI to the edge rather than sending everything to the cloud. Read more.
2:35pm-3:15pm Friday, September 7, 2018
Implementing AI
Location: Imperial B
Alasdair Allan (Babilim Light Industries)
Average rating: *****
(5.00, 1 rating)
Google's AIY Projects kits bring Google's machine learning algorithms to developers with limited experience in the field, allowing them to prototype machine learning applications and smart hardware more easily. Alasdair Allan walks you through setting up and building the kits and demonstrates how to use the kits' Python SDK for machine learning both in the cloud and locally on a Raspberry Pi. Read more.
4:00pm-4:40pm Friday, September 7, 2018
Implementing AI
Location: Imperial A
Noah Schwartz (Quorum AI)
Noah Schwartz explores the most recent advances in cooperative learning systems, including distributed and federated learning systems for real-world, edge-based AI. He also considers the pros and cons of multi-agent systems and demonstrates how Quorum AI is working to bridge the gap with the Quorum AI Framework. Read more.
4:50pm-5:30pm Friday, September 7, 2018
Implementing AI
Location: Imperial B
Vas Chellappa (Pure Storage)
Vas Chellappa explains how to keep your GPUs fed with data as you train the next generation of deep learning architectures and shares a new benchmark suite for evaluating and tuning input pipelines. Read more.