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Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

Speaker slides & video

Presentation slides will be made available after the session has concluded and the speaker has given us the files. Check back if you don't see the file you're looking for—it might be available later! (However, please note some speakers choose not to share their presentations.)

If you are looking for slides and video from 2017, visit the AI Conference 2017 site.

All
Implementing AI, Models and Methods
KC Tung (AT&T)
The adoption of LSTM in touchpoint prediction stems from the need to model customer journey or the conversion funnel as a series of touchpoints. For an advertiser or marketer, taking into account the sequence of events that leads to a conversion will add tremendous value to the understanding of conversion funnel, impact of types of touchpoints, and even identify high potential leads
Implementing AI
Joseph Spisak (Facebook)
Facebook's strength in AI innovation comes from its ability to quickly bring cutting-edge research into large scale production using a multi-faceted toolset. Learn how PyTorch 1.0 helps to accelerate the path from research to production by making AI development more seamless and interoperable.
Implementing AI, Models and Methods
Ankit Jain (Uber)
Personalization is a common theme in social networks and e-commerce businesses. However, personalization at Uber will involve understanding of how each driver/rider is expected to behave on the platform. In this talk, we will focus on how Deep Learning (LSTM's) and Uber's huge database can be used to understand/predict future behavior of each and every user on the platform.
Implementing AI
Kaijen Hsiao (Mayfield Robotics)
Many people have dreamed of having their own personal “Rosie the Robot” in their home. And while we’re moving to a future where robots are becoming a part of our everyday lives, consumers are still skeptical. Join Kaijen Hsiao, CTO of Mayfield Robotics, to learn how she created Kuri, a first-of-its-kind home robot that houses complex technology to create an adorable robot.
Michael Li (The Data Incubator), Zachary Glassman (The Data Incubator)
2-Day Training Please note: to attend, your registration must include Training courses.
In this course, we will be offering a non-technical overview of AI and data science. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.
AI Business Summit, Impact of AI on Business and Society
Jennifer Marsman (Microsoft), Lucas Joppa (Microsoft)
The AI for Earth team at Microsoft helps NGOs apply AI to challenges in conservation biology and environmental science. This session will introduce our main initiatives and share progress on projects that apply AI to agriculture, poacher detection, monitoring the spread of pathogens/hosts, species abundance modeling, and animal identification in camera trap and citizen scientist photography.
AI Business Summit, Impact of AI on Business and Society
AI possesses an incredible potential to help address the challenges of our planet. Drawing on her experience as the Head of AI Foundations and Co-Director of Science for Social Good at IBM Research, Aleksandra Mojsilovic shares innovative examples of applying AI to humanitarian problems and discusses gaps that challenge us from making larger impact with our work.
AI Business Summit, Impact of AI on Business and Society
Varun Arora (Baidu USA)
We haven't figured out how to make the perfect robot tutors. But we have figured out how make them much more effective in improving student learning outcomes with modern AI techniques. This talk will cover some of those important techniques, with real-world examples.
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Jake Saper (Emergence Capital)
Much attention in enterprise AI today is focused on automation. We think the more interesting applications will focus on worker augmentation and call this phenomena Coaching Networks. Learn what Coaching Networks-based companies are, how to build them, and why we think they'll create a company that will dwarf Salesforce in scale.
Implementing AI, Interacting with AI
Daniel Whitenack (Pachyderm)
Tutorial Please note: to attend, your registration must include Tutorials on Wednesday.
In this tutorial, we will learn how to easily deploy and scale AI/ML workflows on any infrastructure using Kubernetes, the container orchestration engine used by all of the top technology companies. Kubernetes was built, from the ground up, to run and manage highly distributed workloads on huge clusters, and, thus, it provides a solid foundation for model development.
AI Business Summit, Impact of AI on Business and Society
Jake Porway (DataKind)
DataKind Founder and Executive Director Jake Porway will shed light on AI’s true potential to impact the world in a positive way. As the head of an organization applying AI for social good, Jake will share best practices, discuss the importance of using human-centered design principles, and address ethical concerns and challenges one may face in using AI to tackle complex humanitarian issues.
AI Business Summit, AI in the Enterprise
Joe Rothermich (Thomson Reuters Labs)
The finance industry, after a slow start, is quickly catching up with others in its adoption of AI. This session will show examples of how Thomson Reuters Labs is using AI to perform research in building quantitative investment models. We’ll discuss our research in deep learning for credit risk, machine learning/NLP for unlocking alternative data sets, and financial graph-based analytics.
AI in the Enterprise, Impact of AI on Business and Society
Gaurav Agarwal (NVIDIA)
Building a self-driving technology which can understand the nuances of the world and drive in all the scenarios is a hard problem. In this talk, the latest trends and challenges in Autonomous driving will be presented. Then the important role of Artificial intelligence/deep learning to enable this technology will be discussed.
Implementing AI, Models and Methods
Sarah Bird (Facebook)
Earlier this year, Amazon, Facebook and Microsoft partnered together to help advance AI together, by creating ONNX. ONNX stands for Open Neural Network Exchange (ONNX). It is an open format to represent deep learning models. This session will explain in detail how the ONNX framework can help you take AI from research to reality as quickly as possible.
AI Business Summit
Kristian Hammond (Northwestern Computer Science)
Tutorial Please note: to attend, your registration must include Tutorials on Wednesday.
Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making.
Models and Methods
David Arpin (Amazon Web Services)
Tutorial Please note: to attend, your registration must include Tutorials on Wednesday.
Outline: Provide a quick product overview of the Amazon SageMaker machine learning platform; Setup and Amazon SageMaker Notebook (hosted Jupyter Notebook server); Hands on training using a built-in SageMaker deep learning algorithm; Dive into building your own neural network architecture using SageMaker's pre-built TensorFlow containers.
Implementing AI, Interacting with AI, Models and Methods
Xiaoyong Zhu (Microsoft), Gheorghe Iordanescu (Microsoft), Wilson Lee (Microsoft Corporation), Ivan Tarapov (Microsoft)
Tutorial Please note: to attend, your registration must include Tutorials on Wednesday.
We will give a tutorial on how to build a deep learning model and build intelligent applications on edge devices including iOS, Android, and Windows. Our working example in this tutorial is from radiology. Chest X-rays are currently playing a crucial role in lung disease detection. How do we power clinicians to identify possible lung diseases in areas with less access to radiologists?
AI Business Summit, AI in the Enterprise
Sean Gourley (Primer AI)
Technology has opened up access to more information than ever before, but it’s still on humans to turn that data into knowledge. To solve this problem organizations are turning to AI and natural language processing to augment human analysts. Join Primer founder, Sean Gourley, as he discusses how the world’s largest organizations use AI to summarize thousands of documents and scale human analysts.
Implementing AI
Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley), Ion Stoica (UC Berkeley)
Tutorial Please note: to attend, your registration must include Tutorials on Wednesday.
Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into 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.
Keynotes
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Closing remarks by Program Chairs Ben Lorica and Roger Chen
Keynotes
Closing remarks by Program Chairs Ben Lorica and Roger Chen
Ben Odom (Intel), Rudy Cazabon (Intel), Meghana Rao (Intel)
2-Day Training Please note: to attend, your registration must include Training courses.
This hands-on training will leave attendees knowing how to build, implement and deploy a deep learning solution. Every registered attendee will receive free hardware to work with during the training. The hardware is yours to keep at the end of the event.
Implementing AI
Yishay Carmiel (IntelligentWire)
In recent years, there has been a quantum leap in the performance of AI. From speech recognition to machine translation and computer vision, deep learning made its mark. However, the more artificial intelligence is gaining popularity, the issues of data privacy are getting more traction. This session will review these issues and how they impact the future of deep learning development.
AI Business Summit, AI in the Enterprise
Given the revolution in data and healthcare, we are reflecting on how we see certain sectors unfold and opportunities for innovation. We will discuss innovations that advance precision medicine by bringing together interdisciplinary fields across biology, engineering, data science, and clinical care.
Implementing AI
Avesh Singh (Cardiogram), Kevin Wu (Cardiogram)
Deep Learning is often called a black box, so how can we diagnose and fix problems in a Deep Neural Network (DNN)? Engineers at Cardiogram explain how they systematically debugged DeepHeart, a DNN that detects cardiovascular disease from heart rate data. You'll leave this talk with an arsenal of tools for debugging DNNs, including Jacobian analysis, TensorBoard, and "DNN Unit Tests".
Models and Methods
Roger Chen (Computable Labs)
Blockchain technologies offer new internet primitives for creating open and online data marketplaces. Roger Chen explores how data markets can be constructed and how they offer a shared resource on the internet for AI-based research, discovery, and development.
Implementing AI, Models and Methods
Ting-Fang Yen (DataVisor)
Online fraud is oftentimes orchestrated by organized crime rings, where malicious user accounts actively target various modern online services for financial gain. In this talk, we present a real-time, scalable fraud detection solution backed by deep learning and built on Spark and Tensorflow. We demonstrate how our system outperforms traditional solutions such as blacklists and machine learning.
Ira Cohen (Anodot), Arun Kejariwal (MZ)
Ira Cohen will present a novel approach for building more reliable prediction models by integrating anomalies in them. And then, Arun Kejariwal will walk the audience through how to marry correlation analysis with anomaly detection, discuss how the topics are intertwined and the challenges one may come across based on production data.
Implementing AI, Interacting with AI, Models and Methods
Anirudh Koul (Microsoft)
Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially in computer vision. In this session, we will talk about bringing the power of deep learning, to memory and power constrained devices like smartphones.
Rich Ott (The Data Incubator)
2-Day Training Please note: to attend, your registration must include Training courses.
BigDL is a powerful tool for leveraging Hadoop and Spark clusters for deep learning. Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL's components and explaining how to use it to implement machine learning algorithms. You'll use your newfound knowledge to build algorithms that make predictions using real-world datasets.
Robert Schroll (The Data Incubator)
2-Day Training Please note: to attend, your registration must include Training courses.
The TensorFlow library provides for the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. This training will introduce TensorFlow's capabilities through its Python interface.
Woj Zaremba (OpenAI)
Deep Reinforcement Learning for Robotics
Models and Methods
Lydia T. Liu (UC Berkeley)
Lydia Liu discusses the results of research on how static fairness criteria interact with temporal indicators of well-being. These results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria and suggest a range of new challenges and trade-offs.
AI Business Summit, Impact of AI on Business and Society
Robin Bordoli (Figure Eight)
AI in the real world is a reality. AI is beginning to do things that are uniquely human, seeing things and labeling, but without contextual details or any human values. Humans and machines need to work together in AI. This talk will highlight the importance of training AI so that its application in the real world goes right.
AI Business Summit, Impact of AI on Business and Society
Chris Butler (Philosophie)
Tutorial Please note: to attend, your registration must include Tutorials on Wednesday.
Purpose, a well-defined problem, and trust from people are important factors to any system, especially those that AI. Chris Butler leads you through exercises that borrow from the principals of design thinking to help you create more impactful solutions and better team alignment.
Implementing AI, Interacting with AI, Models and Methods
Mary Wahl (Microsoft Corporation), Banibrata De (Microsoft)
Tutorial Please note: to attend, your registration must include Tutorials on Wednesday.
High-resolution land cover maps help quantify long-term trends like deforestation and urbanization, but are prohibitively costly and time-intensive to produce. In this tutorial, we demonstrate how we used Microsoft’s Cognitive Toolkit and Azure cloud resources to produce land cover maps from aerial imagery by training a semantic segmentation DNN, both on single VMs and at scale on GPU clusters.
Implementing AI
Magnus Hyttsten (Google)
In this talk, you'll learn how to perform distributed TensorFlow training using the Keras high-level APIs. We will go through the distributed architecture of TensorFlow, set up a distributed cluster using KubeFlow & Kubernetes, and how to distribute models created in Keras.
Implementing AI
Alasdair Allan (Babilim Light Industries)
The 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. We walkthrough how to setup and build the kits, and how to use the kits Python SDK to use machine learning both in the cloud, and locally on the Raspberry Pi.
Implementing AI
Simon Crosby (SWIM Inc.)
This talk presents 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. Attendees will learn that there is more than enough resource at “the edge” to cost-effectively analyze, learn and predict from streaming data on-the-fly.
Vikram Saletore (INTEL CORP), Lucas Wilson (Dell EMC)
SURFSara and Intel collaborated as part of the Intel Parallel Computing Center initiative to advance the state of large-scale neural network training on Intel Xeon(R) CPU based servers. We highlight improved time to solution on extended training of this pretrained model, as well as whether various storage and interconnect options lead to more efficient scaling.
AI Business Summit, Interacting with AI
Danielle Krettek (Google)
We now live in the age of assistive and AI companions, where everything is coming alive. Danielle Krettek, Founder of Google's Empathy Lab, will show you how to design for the "invisible" emotional layer of experience that marks this new wave of technology.
Implementing AI
Shaoshan Liu (PerceptIn)
we describe the technology details of building a reliable autonomous vehicle with < $10,000 total cost.
Implementing AI
Jian Wu (NIO U.S.)
This talk presents an end-to-end engineering work to train and evaluate deep q-learning model(s) for targeting sequential marketing campaigns using 10-Fold cross validation method, we also evaluate trained DQN model(s) with neural network based baseline models and show that trained deep q-learning model does produce better optimized long-term rewards at the majority of 10 testing datasets.
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Risto Miikkulainen (Sentient.ai)
Deep learning (DL) has transformed much of AI, and demonstrated how machine learning can make a difference in the real world. In DL, massive expansion of available training data and compute gave Neural Networks a new instantiation that significantly increased its power. Evolutionary computation (EC) is on the verge of a similar breakthrough. This presentation will explain why EC is the next DL.
AI Business Summit, Impact of AI on Business and Society
Allison Duettmann (Foresight Institute)
The talk starts with an overview of AI philosophy and how traditional approaches need updating because they pave the way for a singleton AI that will hardly be benevolent. I will discuss potential alternative AI safety strategies and their shortcomings and close with a brief survey of interesting problems in AI safety and what we can hope for if we get it right.
AI Business Summit
Deep learning works well when you have large labeled datasets, but not every team has those assets. *Active learning* is a ML variant which incorporates *human-in-the-loop*. It focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore/exploit "uncertainty" in your data. Strategy emerges for managing teams of people + automation.
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Susan Etlinger (Altimeter Group)
In this talk, industry analyst Susan Etlinger explores how AI fundamentally changes the relationship between people and businesses, lays out its risks and opportunities and demonstrates emerging best practices for designing customer-centric and ethical products and services.
AI Business Summit, Impact of AI on Business and Society
Mehdi Miremadi (McKinsey & Company)
Drawing on the McKinsey Global Institute's groundbreaking research, this session will explore some commonly asked questions relating to AI and its impact on work: Ho big is the AI opportunity? Which sectors and functions will capture the most value? As AI takes hold, will there be enough jobs for humans to do? What will they be and what skills are needed? What are the implications for leaders?
AI Business Summit, AI in the Enterprise
Forrest Iandola (DeepScale)
Now more than ever, success in AI requires expertise in multiple disciplines, including big data, efficient software, and novel models/algorithms. In this talk, we present an approach for developing "full-stack" AI teams that have these diverse skills and can execute on industrial-scale AI problems.
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Ben Vigoda (Gamalon)
Is there a “Moore’s Law” for AI: for AI’s to serve every individual customers, we need much more complex natural language understanding, ideas, and behaviors. Will compositional deep learning put us on a new curve?
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Mike Tung (Diffbot)
A new web of information is being built--this time for machines, by machines--and this will lead us to a future of smarter systems that improve our lives and businesses.
AI Business Summit, AI in the Enterprise
Ashok Srivastava (Intuit)
In this session, Intuit Chief Data Officer Ashok Srivastava explores how to make your organization AI ready, how to determine which are the right AI applications for your business and products, and how to accelerate your AI efforts with speed and scale.
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Sharad Gupta (Blue Shield of California)
AI-powered ChatBots are increasingly becoming viable solutions for customer service use cases. Technology leaders need to consider adopting a multi-channel ChatBots strategy to avoid siloed ChatBot solutions. The objective of this executive briefing session is provide the technology executives and the decision-makers with a framework to ensure long-term strategic investment in ChatBots.
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Mariya Yao (Metamaven)
Executives in every business and function are asked to "innovate with AI", but barriers to successful adoption for most enterprises are organizational, not technical. Effective AI requires not only technical talent, but extended interdisciplinary coordination between teams, investments in retraining your workforces at all levels, and cultivation of an experimental, data-driven culture.
AI Business Summit, AI in the Enterprise
David Talby (Pacific AI)
New AI solutions in question answering, chat bots, structured data extraction, text generation and inference all require deep understanding of the nuances of human language. This talk details best practices, challenges & risks in building NLU based systems. The examples and case studies come from real products and services, built by Fortune 500 companies and startups over the past six years.
AI Business Summit, Impact of AI on Business and Society
Amanda Casari (SAP Concur)
Data-driven companies making intelligent products must design for security and privacy to be competitive globally. This talk will outline the high-level changes that EU General Data Protection Regulation (GDPR) compliant businesses face and how this translates to teams designing products driven by machine learning and artificial intelligence.
Implementing AI
Jay Budzik (ZestFinance)
What does it mean to explain a machine learning model, and why is it important? Jay Budzik of ZestFinance will address those questions while discussing several modern explainability methods, including traditional feature contributions, LIME, and DeepLift. Each of these techniques offers a different perspective, and their clever application can reveal new insights and solve business requirements.
AI Business Summit, Impact of AI on Business and Society
Mayank Kejriwal (USC Information Sciences Institute)
Human trafficking is a form of modern day slavery. Online sex advertisement activity on portals like backpage provide important clues that, if harnessed and analyzed at scale, can help resource-strapped law enforcement crack down on trafficking activity. I will describe our efforts in building an AI architecture called DIG that law enforcement have used (and are using) to combat sex trafficking.
Keynotes
Kai-Fu Lee (Sinovation Ventures), Tim O'Reilly (O'Reilly Media)
Fireside chat with Tim O'Reilly and Kai-Fu Lee
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Adam Cutler (IBM Design)
We humans will form relationships with just about anything, our cars, our phones, our pets. Now that AI provides machines the ability to understand, reason, learn, and interact, the building blocks for forming meaningful relationships with machines is now possible. As we move past text fields and submit buttons, what does it mean to design for relationships instead of UIs?
Keynotes
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Opening remarks by Program Chairs Ben Lorica and Roger Chen
AI Business Summit, Impact of AI on Business and Society
Rudina Seseri (Glasswing Ventures), Brian Eberman (Jibo)
Rudina Seseri, Managing Partner, Glasswing Ventures, an early-stage venture capital firm dedicated to investing in the next generation of AI-powered technology companies and Brian Eberman, CEO of Jibo, Inc., makers of the eponymous robot, share their experience launching Jibo, Time Magazine's # 1 Best Invention of the Year and the world's first social robot for the home.
Implementing AI
Emily Watkins (Pure Storage)
Learn how to keep your GPUs fed with the entirety of your data lake as you train the next-generation of deep learning architectures.
AI Business Summit, Impact of AI on Business and Society
Rachael Rekart (Autodesk )
Autodesk Virtual Agent (AVA) has revolutionized the way Autodesk approaches customer service. Built on IBM's cognitive technology, AVA responds to the most common customer questions. Customers chat with AVA as they would a human, in natural language, with AVA returning accurate answers, processing a transaction quickly, or gathering information to pass to a human counterpart to resolve the query.
Implementing AI
Ramesh Sridharan (Captricity)
At Captricity, we've deployed a machine learning pipeline that can read handwriting at human-level accuracy. This talk highlights the big ideas we learned building and deploying this system: using data to identify specific problems to solve using AI, and to evaluate and validate the algorithm itself as well as the overall system once deployed.
Models and Methods
Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft)
Transfer learning enables you to use pre-trained deep neural networks and adapt them for various deep learning tasks (e.g. image classification, question answering, and more). Join Wee Hyong Tok and Danielle Dean, as they share the secrets of transfer learning, and how you can custom these pre-trained models for your use cases, and jumpstart.
AI Business Summit, Impact of AI on Business and Society
Candace Worley (McAfee)
Hear why the future isn’t about AI or machine learning; it’s about human-machine teaming. As long as there are human adversaries behind cybercrime and cyber warfare, there will always be a critical need for the human beings teamed with machines in cybersecurity.
Interacting with AI, Models and Methods
Carl Osipov (Google)
Tutorial Please note: to attend, your registration must include Tutorials on Wednesday.
This hands-on workshop walks through creating increasingly sophisticated image classification models using TensorFlow.
Implementing AI, Interacting with AI, Models and Methods
Piero Molino (Uber AI Labs), Huaixiu Zheng (Uber Applied machine Learning), Yi-Chia Wang (Uber Applied machine Learning)
At Uber we implemented a machine learning and natural language processing system that suggests to our customer support representatives the most likely solutions to a ticket. This makes them faster and more accurate while providing a better user experience. We describe how we built two versions of the system, with traditional and deep learning models, and discuss the lessons learned along the way.
Keynotes
David Patterson (UC Berkeley)
Keynote by David Patterson
Keynotes
Dawn Song (UC Berkeley)
Keynote by Dawn Song
Keynotes
Huma Abidi (Intel)
Keynote by Huma Abidi
Keynotes
Julie Shin Choi (Intel AI)
Keynote by Julie Choi
Keynotes
Kai-Fu Lee (Sinovation Ventures)
Keynote by Kai-Fu Lee
Keynotes
Matt Wood (Amazon Web Services)
Keynote by Matt Wood
Keynotes
Meredith Whittaker (AI Now Institute, NYU)
Keynote by Meredith Whittaker
Keynotes
Peter Norvig (Google)
Keynote by Peter Norvig
Keynotes
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Keynote by Program Chairs Ben Lorica and Roger Chen
Keynotes
Keynotes to come
Keynotes
Keynotes to come
AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society
Paris Buttfield-Addison (Secret Lab Pty. Ltd.), Jonathon Manning (Secret Lab Pty. Ltd.), Tim Nugent (lonely.coffee)
Video games have been using sophisticated AI techniques for decades. Long before many other fields looked to solve their problems using intelligent agents, planning algorithms, and complex computer opponents, video games used AI to drive everything from area design, to navigation, to enemies, to conversation and planning. This session explores AI in games, and what other fields can learn from it.
Implementing AI, Interacting with AI, Models and Methods
Goodman Gu (Atlassian)
Over 400MM people worldwide have some sort of speech/hearing disorders that prevent them from participating in the job market. It is our strong believe that disability should not mean disadvantage. Stride4All is an initiative of using AI to open work up for disabled people and empower them for teamwork. We showcase a prototype built with the latest deep learning and computer vision technologies.
Interacting with AI, Models and Methods
Daniel Golden (Arterys)
Modern radiological lung cancer screening is an entirely manual process, leading to high costs and inter-reader variability. We have developed a deep learning-based system that automatically detects and segments lung nodules in lung CT exams. The system is FDA-cleared and segments nodules as accurately as a clinician. We explain the details of our system and how we proved its safety and efficacy.
Implementing AI, Models and Methods
Abhishek Tayal (Twitter)
This talk will provide a glimpse of how Cortex (ML platform team at Twitter) is developing models, related tooling & infrastructure with the objective of making Entity Embeddings a "First Class Citizen" within Twitters ML platform. Will share success stories on how developing such an ecosystem increases efficiency, productivity and leads to better outcomes across product ML teams.
Delip Rao (R7 Speech Science)
2-Day Training Please note: to attend, your registration must include Training courses.
Delip Rao explores natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.
In the Distiller talk I will briefly introduce the motivation for compressing DNNs and explain in high-level the different types compression approaches, introduce Distiller's design and tools, supported algorithms, and the code and documentation, and show an example implementation of a compression research paper. Close with a look at the next steps
Tom Hanlon (Functional Media)
2-Day Training Please note: to attend, your registration must include Training courses.
Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Tom Hanlon demonstrates how to use Deeplearning4j to build recurrent neural networks for time series data.
2-Day Training Please note: to attend, your registration must include Training courses.
The NVIDIA Deep Learning Institute (DLI) will walk you through the fundamentals of deep learning – training neural networks and using results to improve performance and capabilities. Once you’ve learned the basics, you'll apply deep learning to digital content creation and game development to start creating digital asset with deep learning. No prior deep learning experience required.
AI Business Summit, Impact of AI on Business and Society
Danny Lange (Unity Technologies)
Join this session to discuss the role of intelligence in biological evolution and learning. The speaker will demonstrate why a game engine is the perfect virtual biodome for AI’s evolution. Attendees will recognize how the scale and speed of simulations is changing the game of AI while learning about new developments in reinforcement learning.
Cormac Brick (Intel)
In recent years there has been lost of work done on low precision inference, this shows that by training for quantization, large gains in energy efficiency can be achieved. A look at industry challenges and progress needed to close the portability performance gap.
Implementing AI
Ayin Vala (Foundation for Precision Medicine)
Complex diseases like Alzheimer’s Disease cannot be cured by pharmaceutical or genetic sciences alone and current treatments lead to mixed successes. By introducing more data-driven investigation we can take into account individual variability in medicine intake, lifestyle, genetic factors, and medical images for each person and use the power of big data and AI to treat challenging diseases.
Implementing AI, Models and Methods
Zhou Xing (Borgward R&D Silicon Valley, Inc)
Predictions of driver's intentions and their behaviors using the road is of great importance for planning and decision making processes of autonomous driving vehicles. In this article, we demonstrate a disciplined methodology that can be used to build and train a predictive drive system, therefore to learn the on-road driver intentions, behaviors, associated risks, etc.
Models and Methods
Hari Koduvely (Zighra Inc)
Cyber attacks are becoming increasingly more sophisticated with the use of AI powered tools. To counter these one should use autonomous systems which can adapt to new attack methods and detect them very early. In this presentation I would be talking about how Sequential Anomaly Detection methods implemented using Deep Reinforcement Learning can be used for this purpose.
Implementing AI, Interacting with AI
Labhesh Patel (Jumio)
Labhesh Patel, Jumio’s Chief Scientist, will explore how deep learning is informing our computer vision through smarter data extraction, fraud detection, and risk scoring. Mr. Patel will discuss how Jumio is leveraging massive data sets and human review to dramatically improve the accuracy of our ML algorithms to detect bogus IDs and streamline the verification process of legitimate documents.
Implementing AI, Models and Methods
Mo Patel (Independent), David Mueller (Teradata)
Tutorial Please note: to attend, your registration must include Tutorials on Wednesday.
From photo filter of social networks to self driving cars to detecting skin cancer, Computer Vision has brought applied deep learning to the masses. Built by the pioneers of Computer Vision software, PyTorch is allow developers to rapidly build computer vision models. Learn the fundamental concepts of computer vision and apply them in PyTorch for building computer vision applications.
Kai-Fu Lee (Sinovation Ventures)
Q&A with Kai Fu Lee
Danny Goodman (Switchback Ventures)
Reinforcement Learning and the Future of Software
Implementing AI
Mark Hammond (Bonsai)
Building complex, real-world reinforcement learning systems requires leveraging many techniques including curriculum learning, hierarchical RL, and reward shaping. In this session, Mark Hammond will examine many of these techniques and illustrate how they can be effectively combined into a comprehensive machine teaching program.
Implementing AI
Brian Dalessandro (Zocdoc), Chris Smith (Zocdoc)
With the help of better software, cloud infrastructure and pre-trained networks, AI models have become easier to build. But once your solution veers from a common path, hidden challenges in reproducibility and implementation arise. We cover our experience and learnings building a computer vision and OCR app for reading and classifying insurance cards.
Andrew Feldman (Cerebras Systems)
Session by Andrew Feldman
Sergey Levine (UC Berkeley)
Session by Sergey Levine
Implementing AI, Models and Methods
Evan Sparks (Determined AI), Ameet Talwalkar (Determined AI)
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. We will discuss fundamental challenges facing organizations looking to adopt Deep Learning, and present novel solutions to overcome several of these challenges.
Implementing AI, Interacting with AI
Jason Laska (Clara Labs, Inc.)
Clara’s human-in-the-loop scheduling service combines the precision of machine intelligence and the judgement of an expert team. This session will focus on the tradeoffs between text annotations defined for fast data entry vs. those meant solely for training machine learning models. We’ll use the application of datetime text as it pertains to meeting-attendee availability to guide the discussion.
AI Business Summit, AI in the Enterprise
Beth Partridge (milk+honey), Nick Paquin (milk+honey), Annie O'Connor (milk+honey)
Tutorial Please note: to attend, your registration must include Tutorials on Wednesday.
Utilizing AI technologies to advance business goals remains one of the most daunting challenges for many business leaders. In this interactive session, Beth Partridge offers a breakthrough approach that bridges the gap between data science and business. You will walk away with a clear understanding of what AI can do for your business, and how to go about implementing it.
Implementing AI, Interacting with AI
Derek Murray (Google), Brennan Saeta (Google)
Training data is the lifeblood of a machine learning system, and modern accelerators like GPUs and TPUs are very thirsty for it. The tf.data library provides efficient access to your data in a variety of formats, and a wide range of transformations for augmenting your data. In this talk, we will introduce tf.data and show you how to use it to achieve peak performance in your training pipeline.
Implementing AI
Noah Schwartz (Quorum AI, Inc)
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.
AI Business Summit
Irmak Sirer (IDEO)
The public dialogue around artificial intelligence takes place at extremes, where some choose to focus on how robots will take everyone’s job and others speak of a utopia where AI will do everything from cure cancer to solve world hunger. I believe there’s an alternative to these extreme views of AI. Instead of artificial intelligence, we instead must focus on augmenting people’s intelligence.
Impact of AI on Business and Society
Jana Eggers (Nara Logics), Elsie Kenyon (Nara Logics)
In neuroscience, the wiring diagram of the brain is a connectome. We built a connectome of the AI/ML "brain" via arXiv papers. We'll share the results of how papers, topics, keywords, authors, institutions, publication dates, citations & more are linked & with what strength for interesting insights on how the AI research world is connected, plus give the audience a chance to query the connectome.
Implementing AI, Interacting with AI
Ofer Ronen (Chatbase)
Chatbots are expected to make machine communication feel human. But high-quality bot experiences are very hard to build.
Keynotes
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
Opening remarks by Program Chairs Ben Lorica and Roger Chen
Casimir Wierzynski (Intel AI)
One of the biggest challenges in AI is how to translate advances in the lab into large-scale applications. In this session, we will review current trends in this field and use case studies to illustrate how co-designing these components in concert will be critical for building the AI systems of the future.
Interacting with AI, Models and Methods
A. Besir Kurtulmus (Algorithmia)
Machine Learning algorithms are being developed and improved at an incredible rate, but are not necessarily accessible to the broader community. That’s about to change, thanks to DanKu, a new blockchain-based protocol for evaluating and purchasing ML models on a public blockchain such as Ethereum. DanKu enables anyone to get access to high quality, objectively measured machine learning models.
AI Business Summit, AI in the Enterprise
Mayukh Bhaowal (Salesforce)
Machine learning is eating software. As decisions are automated, model interpretability becomes an integral part of the ML pipeline, rather than an afterthought. In the real world, the demand for being able to explain a model is rapidly gaining on model accuracy. This talk will discuss the steps taken at Salesforce Einstein towards making machine learning transparent and less of a black box.
Implementing AI
Neil Tan (ARM)
What if I told you AI inferencing be done on chips that cost less than a dollar? uTensor lets you do just that. It is a custom Tensorflow runtime for microcontrollers (MCUs), the first framework to streamline model deployments on MCUs. This allow you to push AI to the edge rather than sending everything to cloud. Now, with CMSIS-NN integration, uTensor is faster and more energy efficient.
Joel Hestness (Baidu)
Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. Baidu Research's Silicon Valley AI Lab researches new model architectures and features for speech recognition (Deep Speech 3), speech generation (Deep Voice 3), and natural language processing.