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
 
Grand Ballroom
11:05am No session
1:45pm No session
Yosemite A
Add A practical guide to conducting an AI snake oil sniff test to your personal schedule
11:05am A practical guide to conducting an AI snake oil sniff test Joshua Joseph (Alpha Features)
Add AI within O'Reilly Media to your personal schedule
11:55am AI within O'Reilly Media Paco Nathan (O'Reilly Media)
Add Data science and NLP in the era of deep learning to your personal schedule
2:35pm Data science and NLP in the era of deep learning Yinyin Liu (Intel Nervana)
4:50pm No session
Yosemite BC
Add Deep learning with limited labeled data to your personal schedule
1:45pm Deep learning with limited labeled data Avesh Singh (Cardiogram), Brandon Ballinger (Cardiogram)
Add Applying AI to healthcare's biggest opportunity: Clinical variation to your personal schedule
2:35pm Applying AI to healthcare's biggest opportunity: Clinical variation Todd Stewart (Mercy), Lonny Northrup (Intermountian)
Add Highly dense modular acceleration clusters for deep learning to your personal schedule
4:50pm Highly dense modular acceleration clusters for deep learning Bharadwaj Pudipeddi (NVXL Technology)
Imperial A
Add Bringing gaming, VR, and AR to life with deep learning to your personal schedule
2:35pm Bringing gaming, VR, and AR to life with deep learning Danny Lange (Unity Technologies)
Add Deep learning in enterprise IoT: Use cases and challenges to your personal schedule
4:50pm Deep learning in enterprise IoT: Use cases and challenges Jisheng Wang (Aruba, a Hewlett Packard Enterprise Company)
Imperial B
Add How AI is ushering in a new era of healthcare to your personal schedule
11:05am How AI is ushering in a new era of healthcare Vijay Pande (Andreessen Horowitz)
Add AI and cellular images for universal drug discovery to your personal schedule
11:55am AI and cellular images for universal drug discovery Blake Borgeson (Recursion Pharmaceuticals), Nan Li (Obvious Ventures)
Add Very large-scale distributed deep learning with BigDL to your personal schedule
2:35pm Very large-scale distributed deep learning with BigDL Jason Dai (Intel), Ding Ding (Intel)
Add Scaling CNNs with Kubernetes and TensorFlow to your personal schedule
4:00pm Scaling CNNs with Kubernetes and TensorFlow Reza Zadeh (Matroid & Stanford)
Add Scalable operationalization of trained CNTK and TensorFlow DNNs to your personal schedule
4:50pm Scalable operationalization of trained CNTK and TensorFlow DNNs Mary Wahl (Microsoft Corporation)
Franciscan AB
Add Intelligent applications are everywhere. to your personal schedule
1:45pm Intelligent applications are everywhere. Matt McIlwain (Madrona Venture Group), Carlos Guestrin (Apple | The University of Washington)
Add The potential ick factor: Ethical considerations for designing in healthcare to your personal schedule
2:35pm The potential ick factor: Ethical considerations for designing in healthcare Astrid Chow (IBM Watson Health), Amy Chenault (Insulet), Joel Wu (Children's Minnesota)
Add Next-generation intelligent applications require cognitive design. to your personal schedule
4:00pm Next-generation intelligent applications require cognitive design. John Whalen (Brilliant Experience)
Add Building an unbiased AI: End-to-end diversity and inclusion in AI development to your personal schedule
4:50pm Building an unbiased AI: End-to-end diversity and inclusion in AI development Daniel Guillory (Autodesk), Matthew Scherer (Littler Mendelson, PC )
Franciscan CD
Add Accelerating deep learning to your personal schedule
1:45pm Accelerating deep learning Bill Jenkins (Intel)
Add Tuesday opening remarks to your personal schedule
Grand Ballroom B
8:50am Tuesday opening remarks Ben Lorica (O'Reilly Media), Roger Chen
Add The inevitable merger of IQ and EQ in technology to your personal schedule
8:55am The inevitable merger of IQ and EQ in technology Rana el Kaliouby (Affectiva)
Add The state of AI adoption to your personal schedule
9:20am The state of AI adoption Ben Lorica (O'Reilly Media), Roger Chen
Add Deep learning to fight cancer: Fireside chat to your personal schedule
9:25am Deep learning to fight cancer: Fireside chat Peter Norvig (Google), Abu Qader (GliaLab)
Add Fast-forwarding AI in the data center to your personal schedule
9:40am Fast-forwarding AI in the data center | Room: Grand Ballroom B Lisa Spelman (Intel)
Add How AI is ushering in a new era of healthcare to your personal schedule
9:55am How AI is ushering in a new era of healthcare Vijay Pande (Andreessen Horowitz)
Add AI is the new electricity. to your personal schedule
10:05am AI is the new electricity. Andrew Ng (Coursera)
Add Closing remarks to your personal schedule
10:30am Closing remarks Ben Lorica (O'Reilly Media), Roger Chen
Add Tuesday Topic Tables at lunch to your personal schedule
12:35pm Lunch sponsored by IBM Watson Tuesday Topic Tables at lunch | Room: Grand Ballroom B
10:35am Morning Break | Room: Sponsor Pavilion
3:15pm Afternoon Break | Room: Sponsor Pavilion
Add Sponsor Pavilion Reception to your personal schedule
5:30pm Sponsor Pavilion Reception | Room: Sponsor Pavilion
Add AI at Night: Taste of SF to your personal schedule
6:30pm AI at Night: Taste of SF | Room: The Ferry Building
Add Speed Networking to your personal schedule
8:10am Speed Networking | Room: Yosemite Foyer
11:05am-12:35pm (1h 30m)
Session: No session
1:45pm-2:25pm (40m)
Session: No session
2:35pm-3:15pm (40m) Implementing AI Algorithms, Data science and AI
The operating system for AI: How microservices and serverless computing enable the next generation of machine intelligence
Kenny Daniel (Algorithmia)
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.
4:00pm-4:40pm (40m) Implementing AI Algorithms, Tools and frameworks
Using GPU acceleration with PyTorch to make your algorithms 2,000% faster
Jeremy Howard (fast.ai)
Although most devs are aware of the benefits of GPU acceleration, many assume that the technique is only applicable to specialist areas like deep learning and that learning to program a GPU takes complex specialist knowledge. Jeremy Howard explains how to easily harness the power of a GPU using a standard clustering algorithm with PyTorch and demonstrates how to get a 20x speedup over NumPy.
4:50pm-5:30pm (40m) Transportation and autonomous vehicles
Using deep learning and Google Street View to estimate the demographic makeup of the US
Timnit Gebru (Microsoft Research)
Targeted socioeconomic policies require an accurate understanding of a country’s demographics, and the US spends more than $1 billion a year gathering such data. Timnit Gebru shares a solution that leverages Google Street View images and a computer vision pipeline to predict income, carbon emission, crime rates, and other city attributes from a single source of publicly available data.
11:05am-11:45am (40m)
A practical guide to conducting an AI snake oil sniff test
Joshua Joseph (Alpha Features)
As artificial intelligence (and specifically machine learning) firmly takes hold in industry, there has been a significant increase in the amount of AI snake oil being developed, pitched, and sold. Joshua Joseph shares a practical guide for detecting AI products of questionable value or benefit, whether intentional or not.
11:55am-12:35pm (40m) Verticals and applications Media
AI within O'Reilly Media
Paco Nathan (O'Reilly Media)
Paco Nathan explains how O'Reilly employs AI, from the obvious (chatbots, case studies about other firms) to the less so (using AI to show the structure of content in detail, enhance search and recommendations, and guide editors for gap analysis, assessment, pathing, etc.). Approaches include vector embedding search, summarization, TDA for content gap analysis, and speech-to-text to index video.
1:45pm-2:25pm (40m) Implementing AI Deep learning, IoT (including smart cities, manufacturing, smart homes/buildings)
Embedded deep learning: Deep learning for embedded systems
Siddha Ganju (Deep Vision)
Deep learning is necessary to bring intelligence and autonomy to the edge. Siddha Ganju offers an overview of Deep Vision's solution, which optimizes both the hardware and the software, and discusses the Deep Vision embedded processor, which is optimized for deep learning and computer vision and offers 50x higher performance per watt than existing embedded GPUs without sacrificing programmability.
2:35pm-3:15pm (40m)
Data science and NLP in the era of deep learning
Yinyin Liu (Intel Nervana)
Deep learning is providing new opportunities for and solutions to natural language processing problems, enabling new approaches for text, language, and conversation-based use cases. Yinyin Liu shares the latest NLP advances, practices, and resources for data and explores enterprise NLP use cases using the Intel Nervana platform.
4:00pm-4:40pm (40m)
The low-power silicon platforms fueling the new era of vision-based AI
Gary Brown (Intel)
Gary Brown shares the under-the-hood technologies that are enabling devices to function at lower power and with more sophisticated deep learning computation, discussing the new innovations at the silicon level fueling the realization of visually intelligent things.
4:50pm-5:30pm (40m)
Session: No session
11:05am-11:45am (40m) Implementing AI Anomaly detection, Data science and AI
Learning the learner: Using machine learning to monitor. . .machine learning?
Ira Cohen (Anodot)
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.
11:55am-12:35pm (40m) Implementing AI Data science and AI, Tools and frameworks
Ray: A distributed execution framework for reinforcement learning applications
Ion Stoica (UC Berkeley)
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.
1:45pm-2:25pm (40m) Implementing AI Data and training, Medicine
Deep learning with limited labeled data
Avesh Singh (Cardiogram), Brandon Ballinger (Cardiogram)
Deep learning is fueled by large labeled datasets, but in domains like medicine, each label represents a human life at risk. Avesh Singh and Brandon Ballinger offer an overview of autoencoders, heuristic training, and few-shot learning, with an emphasis on practical tips to create high-performing models utilizing hundreds of thousands of unlabeled data points and only thousands of labeled points.
2:35pm-3:15pm (40m) Implementing AI Enterprise adoption, Healthcare
Applying AI to healthcare's biggest opportunity: Clinical variation
Todd Stewart (Mercy), Lonny Northrup (Intermountian)
Mercy and Intermountain, two of the largest and most innovative hospital systems in the United States, have recently applied AI to tackle clinical variation within their systems. Todd Steward and Lonny Northrup discuss the application of machine intelligence for optimizing care and provide valuable insights into practice variation for improving clinical pathways.
4:00pm-4:40pm (40m) Implementing AI Algorithms, Architectures
Choosing a high-performance computing development direction for original algorithms
Art Popp (ServiceNow)
Art Popp walks you through a “from scratch" implementation of two algorithms to demonstrate the tools available for original algorithm development, using both SIMD and SIMT designs, the leading hardware architectures of which are Xeon Phi and NVIDIA Cuda. Along the way, Art explores the performance per watt, performance per dollar (initial cost), and performance per dollar (TCO) of each.
4:50pm-5:30pm (40m) Implementing AI Deep learning, Infrastructure
Highly dense modular acceleration clusters for deep learning
Bharadwaj Pudipeddi (NVXL Technology)
Bharadwaj Pudipeddi proposes a highly dense modular acceleration cluster completely disaggregated from generic servers in the data center that is specifically targeted for deep learning- and AI-related workloads. This cluster is scalable and lightweight (and devoid of Xeons) with the ability to run very deep neural networks through data and model parallelism for extreme performance.
11:05am-11:45am (40m) Impact on business and society Case studies, Deep learning
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)
Pinterest’s power is grounded in its personalization systems. Over the years, these recommender systems have evolved through different types of models. Xiaofang Chen and Derek Cheng explore Pinterest's recent transition from a GBDT system to one based in neural networks powered by TensorFlow, covering the challenges and solutions to providing recommendations to over 160M monthly active users.
11:55am-12:35pm (40m) Implementing AI Deep learning, Technical best practices
Backing off toward simplicity: Understanding the limits of deep learning
Stephen Merity (Salesforce Research)
Deep learning is used broadly at the forefront of research, achieving state-of-the-art results across a variety of domains. However, that doesn't mean it's a fit for all tasks—especially when the constraints of production are considered. Stephen Merity investigates what tasks deep learning excels at, what tasks trigger a failure mode, and where current research is looking to remedy the situation.
1:45pm-2:25pm (40m) Verticals and applications Data science and AI
How Instacart is using AI to create the most efficient shoppers ever
Jeremy Stanley (Instacart)
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.
2:35pm-3:15pm (40m) Implementing AI Deep learning, Tools and frameworks
Bringing gaming, VR, and AR to life with deep learning
Danny Lange (Unity Technologies)
Game development is a difficult and time-consuming pursuit that requires highly skilled labor to succeed. Drawing on his experience at Unity, Danny Lange demonstrates how deep learning and deep reinforcement learning can help developers at various stages in the development process create awesome digital experiences in gaming, VR, and AR.
4:00pm-4:40pm (40m) Interacting with AI Deep learning, Transportation and autonomous vehicles
Software and hardware breakthroughs for deep neural networks at the edge
Michael B. Henry (Mythic)
Breakthroughs in deep learning and new analog-domain computation methods to deploy trained neural networks will deliver exciting new capabilities. Michael B. Henry explains why the combination of human-like levels of recognition and massive computation capabilities in a tiny package will enable products with true awareness and understanding of the user and environment.
4:50pm-5:30pm (40m) Verticals and applications Deep learning, IoT (including smart cities, manufacturing, smart homes/buildings)
Deep learning in enterprise IoT: Use cases and challenges
Jisheng Wang (Aruba, a Hewlett Packard Enterprise Company)
Recently, both deep learning and the IoT have attracted tremendous attention. Jisheng Wang shares firsthand experience in applying deep learning to solving some real-world enterprise IoT problems (e.g., IoT device identification and IoT security) and outlines some challenges for deep learning in enterprise applications, along with suggestions to overcome them.
11:05am-11:45am (40m)
How AI is ushering in a new era of healthcare
Vijay Pande (Andreessen Horowitz)
Expanding on his keynote, Vijay Pande explains how machine learning techniques together with the recent explosion in data are leading to a new approach to prevention, allowing us to more effectively tackle some of the deadliest and costliest health challenges, including heart disease, cancer, and Type 2 diabetes.
11:55am-12:35pm (40m) Verticals and applications Biopharmaceuticals, Deep learning
AI and cellular images for universal drug discovery
Blake Borgeson (Recursion Pharmaceuticals), Nan Li (Obvious Ventures)
Blake Borgeson and Nan Li offer a technical overview of how Recursion—a company that applies computer vision and machine learning to create a high-dimensional feature space in which to evaluate cellular health broadly across hundreds of disease states—leverages cellular phenotyping for drug discovery.
1:45pm-2:25pm (40m)
A visual and intuitive understanding of deep learning
Otavio Good (Google)
Otavio Good demonstrates how Word Lens (part of Google Translate) uses machine learning to detect and translate printed text and explores various other machine learning concepts and their significance.
2:35pm-3:15pm (40m) Implementing AI Data science and AI, Deep learning, Tools and frameworks
Very large-scale distributed deep learning with BigDL
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.
4:00pm-4:40pm (40m)
Scaling CNNs with Kubernetes and TensorFlow
Reza Zadeh (Matroid & Stanford)
Reza Zadeh presents a Kubernetes deployment on Amazon AWS that provides customized computer vision to a large number of users.
4:50pm-5:30pm (40m) Implementing AI Technical best practices, Tools and frameworks
Scalable operationalization of trained CNTK and TensorFlow DNNs
Mary Wahl (Microsoft Corporation)
Mary Wahl shares a cloud-based Hadoop ecosystem solution for deploying deep neural networks (DNNs) with scalable compute resources to accommodate changing workloads and demonstrates how to apply trained Microsoft CNTK and TensorFlow DNNs to a large image set in HDFS (Azure Data Lake Store) using the Python bindings for these deep learning frameworks and a Microsoft HDInsight Spark cluster.
11:05am-11:45am (40m) Implementing AI Algorithms, Bots, Branding and marketing, Case studies, Decision making, Enterprise adoption, Law, ethics and governance (including AI safety), Organizational best practices, Smart Bot
The conversational business: Use cases and best practices for chatbots in the enterprise
Susan Etlinger (Altimeter Group)
Drawing on her report The Conversational Business: How Chatbots Will Reshape Digital Experiences, Susan Etlinger shares use cases, emerging best practices, and design and CX principles from organizations building consumer-facing chatbots.
11:55am-12:35pm (40m) Verticals and applications Algorithms, Finance
The AI revolution’s impact on the financial services industry
Andy Steinbach (NVIDIA)
Andy Steinbach shares case studies and applications in artificial intelligence that are having an impact on financial markets.
1:45pm-2:25pm (40m) Implementing AI Data and training, Visualization and Interfaces
Intelligent applications are everywhere.
Matt McIlwain (Madrona Venture Group), Carlos Guestrin (Apple | The University of Washington)
Matt McIlwain interviews Carlos Guestrin. Drawing on his experience as an AI pioneer, Carlos discusses the intelligent applications powered by data and data science that are being built and deployed at a rapid pace, including on smartphones and edge devices, and shares consumer, commercial, and embedded examples.
2:35pm-3:15pm (40m) Impact on business and society Healthcare, Law, ethics and governance (including AI safety)
The potential ick factor: Ethical considerations for designing in healthcare
Astrid Chow (IBM Watson Health), Amy Chenault (Insulet), Joel Wu (Children's Minnesota)
With great cognitive computing comes great responsibility. As AI becomes ubiquitous in our society, it's critical to discuss the ethical concerns of AI and ask the tough questions. This multidisciplinary roundtable opens a dialogue on how bioethical principles might be applied to everyday design practice within healthcare.
4:00pm-4:40pm (40m) Interacting with AI Data and training, Visualization and Interfaces
Next-generation intelligent applications require cognitive design.
John Whalen (Brilliant Experience)
John Whalen explores the concept of cognitive design, describing how humans structure their commands to AI systems (syntax, word usage, prosody) and how to measure human reactions to AI responses using biometrics (facial emotion recognition, heart rate, GSR). Along the way, John shares insights into how to optimally architect the customer experience.
4:50pm-5:30pm (40m) Impact on business and society Law, ethics and governance (including AI safety), Organizational best practices
Building an unbiased AI: End-to-end diversity and inclusion in AI development
Daniel Guillory (Autodesk), Matthew Scherer (Littler Mendelson, PC )
Diversity has many dimensions relevant to AI development. If designers don't consider and integrate diversity from the very beginning, they risk creating systems that are irrelevant to excluded groups and worse, make excluded groups irrelevant. Daniel Guillory and Matthew Scherer discuss the importance of ensuring diversity and inclusion when developing AI and share tips on how to do so.
11:05am-11:45am (40m) Sponsored
Engineering the future of AI for businesses (sponsored by IBM Watson)
Ruchir Puri (IBM)
Ruchir Puri expands on his keynote address, exploring the opportunities and challenges of AI for business and focusing on what is needed to truly scale out AI applications and systems across the breadth of enterprises.
11:55am-12:35pm (40m) Sponsored
Why complementary learning is the future of AI (sponsored by Intel Saffron)
Bruce Horn (Intel)
Deep learning needs cognitive memory and vice versa. In complementary learning, both forms work together to build a more complete AI system. Bruce Horn explores Intel's Saffron's cognitive approach, which provides one-shot learning using associative and episodic memories and is more appropriate for individual and dynamic patterns.
1:45pm-2:25pm (40m)
Accelerating deep learning
Bill Jenkins (Intel)
Field-programmable gate arrays (FPGAs) provide deterministic low latency and highly efficient implementations with various levels of precision due to their customizable architecture.​ Bill Jenkins shares Intel's deep learning accelerator library, which offers a variety of primitives and architectures highly optimized for FPGAs and allows seamless integration into the Intel ecosystem.
2:35pm-3:15pm (40m) Implementing AI Decision making, Deep learning, Robotics, Transportation and autonomous vehicles
Deep reinforcement learning: Recent advances and frontiers
Li Erran Li (Uber)
Deep reinforcement learning has enabled artificial agents to achieve human-level performance across many challenging domains (for example, playing Atari games and Go). Li Erran Li shares several important algorithms, discusses major challenges, and explores promising results.
4:00pm-4:40pm (40m)
Deep learning in the enterprise: Opportunities and challenges
Ron Bodkin (Teradata)
Tools, frameworks, access to high-value data, and practical approaches to deployment and integration with existing systems and applications are just some of the considerations facing companies adopting deep learning. Ron Bodkin explores tools, open source technology, frameworks, and strategies to cost-effectively achieve strategic results with deep learning in the enterprise.
4:50pm-5:30pm (40m)
Intel Nervana Graph: A universal deep learning compiler
Jason Knight (Intel)
With the chaotic and rapidly evolving landscape around deep learning, we need deep learning-specific compilers to enable maximum performance in a wide variety of use cases on a wide variety of hardware platforms. Jason Knight offers an overview of the Intel Nervana Graph project, which was designed to solve this problem.
8:50am-8:55am (5m)
Tuesday opening remarks
Ben Lorica (O'Reilly Media), Roger Chen (.)
Program chairs Ben Lorica and Roger Chen open the first day of keynotes.
8:55am-9:10am (15m)
The inevitable merger of IQ and EQ in technology
Rana el Kaliouby (Affectiva)
Rana el Kaliouby lays out a vision for an emotion-enabled world of technology, sharing the inner workings of a multimodal emotion sensing platform that identifies emotions through facial expressions and tone of voice. Along the way, Rana explores the broad applications and ethical implications of this technology.
9:10am-9:20am (10m) Sponsored Keynote
Engineering the future of AI for businesses (sponsored by IBM Watson)
Ruchir Puri (IBM)
Ruchir Puri explores the opportunities and challenges of AI for business, focusing on what is needed to truly scale out AI applications and systems across the breadth of enterprises.
9:20am-9:25am (5m)
The state of AI adoption
Ben Lorica (O'Reilly Media), Roger Chen (.)
Program chairs Ben Lorica and Roger Chen kick off the O'Reilly AI Conference in San Francisco with an overview of the current trends they have observed in the industry.
9:25am-9:40am (15m)
Deep learning to fight cancer: Fireside chat
Peter Norvig (Google), Abu Qader (GliaLab)
Abu Qader’s personal experience is a testament to the increasing impact and accessibility of AI technology. As a high school student, he taught himself machine learning using open online resources and launched an AI company for breast cancer diagnostics. Peter Norvig sits down with Abu to share anecdotes, discuss the state of artificial intelligence, and explore where things are heading.
9:40am-9:55am (15m)
Fast-forwarding AI in the data center
Lisa Spelman (Intel)
Lisa Spelman explains how businesses are already benefiting from the industry’s most flexible and most optimized solutions for AI and how Intel is fostering the continued growth of the AI ecosystem so that you too can fast-forward AI in the data center.
9:55am-10:05am (10m)
How AI is ushering in a new era of healthcare
Vijay Pande (Andreessen Horowitz)
Vijay Pande explains how machine learning techniques are leading to a new approach to prevention, allowing us to more effectively tackle some of the deadliest and costliest health challenges, including heart disease, cancer, and Type 2 diabetes.
10:05am-10:30am (25m)
AI is the new electricity.
Andrew Ng (Coursera)
Much like the rise of electricity, which started about 100 years ago, AI will revolutionize every major industry. Andrew Ng explains how AI can transform your business, shares major technology trends and thoughts on where your biggest future opportunities may lie, and explores best practices for incorporating AI, machine learning, and deep learning into your organization.
10:30am-10:35am (5m)
Closing remarks
Ben Lorica (O'Reilly Media), Roger Chen (.)
Program chairs Ben Lorica and Roger Chen close the first day of keynotes.
12:35pm-1:45pm (1h 10m)
Tuesday Topic Tables at lunch
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
10:35am-11:05am (30m)
Break: Morning Break
3:15pm-4:00pm (45m)
Break: Afternoon Break
5:30pm-6:30pm (1h)
Sponsor Pavilion Reception
Come enjoy delicious snacks and beverages with fellow AI Conference attendees, speakers, and sponsors.
6:30pm-9:00pm (2h 30m)
AI at Night: Taste of SF
Join us at San Francisco's iconic Ferry Building for AI at Night: Taste of SF to enjoy food and drinks from some of our favorite local eateries.
8:10am-8:40am (30m)
Speed Networking
Gather before keynotes on Tuesday and Wednesday morning for a speed networking event. Enjoy casual conversation while meeting new attendees.