AI NEXTCon San Francisco '18 completed on 4/10-13, 2018 in Silicon Valley. See you at the next conference in Seattle January 2019. AI NEXTCon Seattle '19. Learn and practice AI online with 500+ tech speakers, 70,000+ developers globally, with online tech talks, crash courses, and bootcamps, Learn more
Speakers
Jeff Dean
Senior Fellow
Google
Jeff Dean joined Google in 1999 and is currently a Google Senior Fellow in Google's Research Group, where he co-founded and leads the Google Brain team, Google's deep learning and artificial intelligence research team. He and his collaborators are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google's crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google's initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google's distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing and the Mark Weiser Award.
Jure Leskovec
Chief Scientist
Pinterest
Jure Leskovec is the Chief Scientist at Pinterest alongside also being an associate professor at Stanford. At Pinterest he leads the Applied Science organization as well as Pinterest Labs. At Stanford, his research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Further back he co-founded "Kosei" which was then acquired by Pinterest. He has a PhD in Machine Learning from Carnegie Mellon University and a Postdoc in Computer Science from Cornell.
Yuandong Tian
Research Scientist
Facebook
Yuandong Tian is a Research Scientist in Facebook AI Research, working on deep reinforcement learning in games and theoretical analysis of deep non-convex models. He is the lead researcher and engineer for DarkForest (Facebook Computer Go project). Prior to that, he was a Software Engineer/Researcher in Google Self-driving Car team. He is the recipient of 2013 ICCV Marr Prize Honorable Mentions for his work on global optimal solution to non-convex optimization in image alignment.
Sarah Aerni
Director of Einstein
Salesforce
Sarah Aerni is a Director of Data Science at Salesforce Einstein, where she leads teams building AI-powered applications across the Salesforce platform. Prior to Salesforce she led the healthcare & life science and Federal teams at Pivotal. Sarah obtained her PhD from Stanford University in Biomedical Informatics, performing research at the interface of biomedicine and machine learning. She also co-founded a company offering expert services in informatics to both academia and industry.
Chester Chen
Head of Data Science
GoPro
Chester is the Head of Data Science & Engineering at GoPro. Before join GoPro, Chester was the Director of Engineering of Alpine Data Labs, a machine learning startup that provide analytics platform for Fortune 500 companies. He is also the founder and organizer of SF Big Analytics meetup, with 6900+ members. Previously, he holds various positions at Symantec, Ascent Media and many other companies. He has given talks in SF Scala Meetup, Big Data Scala, Hadoop Summit and IEEE Big Data Confere.
Jennifer Prendki
Head of Data Science
Atlassian
Dr. Jennifer Prendki is the Head of Data Science at Atlassian, where she leads all Search and Machine Learning initiatives and is in charge of leveraging the massive amount of data collected by the company to load the suite of Atlassian products with smart features. She received her PhD in Particle Physics from University UPMC - La Sorbonne in 2009 and has since that worked as a data scientist for many different industries. Prior to joining Atlassian, Jennifer was a Senior Data Science Manager in the Search team of Walmart eCommerce. She enjoys addressing both technical and non-technical audiences at conferences and sharing her knowledge and experience with aspiring data scientists.
Sunil Mallya
Solutions Architect
AWS Deep Learning
Sunil Mallya is sr. solutions architect focused on deep learning at AWS, where he works with customers in various industry verticals. Previously, he cofounded the neuroscience- and machine learning-based image analysis and video thumbnail recommendation company Neon labs and worked on building large-scale low-latency systems at Zynga. He hold a master’s degree in computer science from Brown University.
Rajat Monga
Engineering Director
Google Brain
Rajat Monga leads TensorFlow at the Google Brain team, powering machine learning research and products worldwide. As a founding member of the team he has been involved in co-designing and co-implementing DistBelief and more recently TensorFlow, an open source machine learning system. Prior to this role, he led teams in AdWords, built out the engineering teams and co-designed web scale crawling and content-matching infrastructure at Attributor, co-implemented and scaled eBay’s search engine and designed and implemented complex server systems across a number of startups.
Martin Gorner
Software Engineer
Google
Software Engineer at Google. best known for his "deep learning without a phd degree" learning series.
Manohar Paluri
Sr. Research Manager
Facebook
Joe Xie
Tech Lead
Twitter
Tech Lead at Twitter.
Romer Rosales
Director of AI
LinkedIn
Romer has held various scientific positions in industry and has also help founding a consumer healthcare start-up as chief scientist. Currently he is a Director of Artificial Intelligence, Head of Flagship Relevance at LinkedIn. His team focuses on machine learning and optimization to make LinkedIn consumer products more personalized, provide higher member value, and ultimately create economic opportunity. He has published over 60 articles in Machine Learning, Data Mining, and Computer Vision journals and conferences, and holds more than a dozen issued US patents in these fields. He has served as organizer and senior committee member for various conferences in the machine learning fields including NIPS, ICML, AISTATS, KDD and as guest editor in various machine learning and data mining journals.
Hope Wang
Software Engineer
Intuit
Hope Wang is a software engineer in Intuit’s Small Business Data and Analytics group. Hope is a self-taught, self-motivated, fully powered hacker with practical abilities and experience, collaborative, and passionate about innovation, she is confident and produces quality work. She holds a master’s degree in Biomedical Engineering from the University of Southern California.
Marek Sadowski
Developer
IBM
Marek Sadowski is a full stack developer advocate, a robotics startup founder and an entrepreneur. He has about 20 year experience in consulting largest enterprises in USA, Europe, Middle East and Africa as a senior engineer and an IT architect in mobile, web, Java and integration technologies. As a graduate from the International Space University Marek pioneered in a research on VR goggles for the virtual reality system to control robots on Mars in NASA Ames. He founded a startup to deliver robotics solutions and services for space, military, and industrial sectors.
Xiaobing Liu
Staff Engineer
Google Brain
Xiaobing Liu, Google Brain Staff software engineer and machine learning researcher since 2014. In his work, Xiaobing focuses on Tensorflow and some key applications where Deep learning could be applied to improve Google products, such as Google Ads, Google Play recommendation and Google translation and Medical Brain. His research interests span from system to the applied machine learning e.g ASR, machine translation, medical EHR modeling, recommendation modeling. His research contributions have been successfully implemented into various commercial products at Tencent, Yahoo. and Google. He has served in the program committees for ACL 2017 and session chair for AAAI 2017, including some publications at some top conferences.
Xavier Amatriain
CTO
Curai
Xavier Amatriain is currently co-founder and CTO of Curai, a stealth startup trying to radically improve healthcare for patients by using AI. Previous to this, he was VP of Engineering at Quora, and Research/engineering Director at Netflix, where he led the team building the famous Netflix recommendation algorithms. Before going into leadership positions in industry, Xavier was a research scientist at Telefonica Research and a research director at UCSB. With over 50 publications (and 3k+ citations) in different fields, Xavier is best known for his work on machine learning in general and recommender systems in particular.
Suqiang Song
Director of AI Platform
Mastercard
Suqiang Song is director and chapter leader at Mastercard, where directly oversees a team embedded within the data engineering and AI tribe. Suqiang blends deep business and technical expertise with a passion for coaching people, helping them grow and develop in their area of expertise and ensuring alignment on the “how” of the work they perform in squads.
Chris Moody
Manager of AI
Stitch Fix
Chris came from a Physics background from Caltech and UCSC, and is now leading Stitch Fix's AI Instruments team. He has an avid interest in NLP, has dabbled in deep learning, variational methods, and Gaussian Processes. He's contributed to the Chainer Deep learning library (http://chainer.org/), the super-fast Barnes-Hut version of t-SNE to scikit-learn (http://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html) and written (one of the few!) sparse tensor factorization libraries in Python (https://github.com/stitchfix/ntflib).
Tony Qin
Staff Researcher
Didi Chuxing
.
Siddha Ganju
Data Scientist
Deep Vision
Siddha graduated from Carnegie Mellon University with a Master's in Computational Data Science. Her work ranges from Visual Question Answering to Generative Adversarial Networks to gathering insights from CERN's petabyte scale data and has been published at top tier conferences like CVPR. She is a frequent speaker at Strata+Hadoop & Strata AI conferences and advises the Data Lab at NASA. When not working, you might catch her hiking!
Jeremy Hermann
Head of ML Platform
Uber
I lead Uber's Machine Learning Platform team. Our mission is to enable engineers and data scientists across the company to easily build and deploy machine learning solutions at scale.
Kevin Murphy
Research Scientist
Google
Kevin Murphy is a research scientist at Google where he works on AI, machine learning, and computer vision. Before joining Google in 2011, he was an associate professor (with tenure) of computer science and statistics at the University of British Columbia in Vancouver, Canada. Before starting at UBC in 2004, he was a postdoc at MIT. Kevin got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He has published over 80 papers in refereed conferences and journals, as well as an 1100-page textbook called "Machine Learning: a Probabilistic Perspective" (MIT Press, 2012), which was awarded the 2013 DeGroot Prize for best book in the field of Statistical Science. Kevin was also the (co) Editor-in-Chief of JMLR (the Journal of Machine Learning Research) 2014-2017.
Junhua Wang
Partner Dev Manager
Microsoft
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Peter Vajda
Research Manager
Facebook
I am a research manager, working on mobile vision efforts at Facebook. Before joining Facebook, I was Visiting Assistant Professor in Professor Bernd Girod's group in Stanford University, Stanford, USA. I was working on personalized multimedia system and mobile visual search. I received M.Sc. in Computer Science from the Vrije Universiteit, Amsterdam, Netherlands and a M.Sc. in Program Designer Mathematician from Eötvös Loránd University, Budapest, Hungary. I completed my Ph.D. at the Ecole Polytechnique Fédéral de Lausanne (EPFL), Lausanne, Switzerland, 2012.
Leah McGuire
Principle Engineer
Salesforce
Leah McGuire is a Principal Member of Technical Staff at Salesforce, working on automating as many of the steps involved in machine learning as possible. Before joining Salesforce, Leah was a Senior Data Scientist on the data products team at LinkedIn. She completed a PhD and a Postdoctoral Fellowship in Computational Neuroscience at the University of California, San Francisco, and at University of California, Berkeley, where she studied the neural encoding and integration of sensory signals.
Avneesh Saluja
Research Scientist
Airbnb
Avneesh is currently a Research Scientist in the AI Lab at Airbnb, where he works on problems of Airbnb's relevance in the machine learning and natural language processing domains. He has concentrated on leveraging the vast amounts of text data on the site to enable the next generation of data products within the company. Avneesh completed his PhD in natural language processing from Carnegie Mellon University in 2015 (where his thesis focused on building contextually richer models for translating human languages), and his undergraduate degree in electrical engineering from Stanford University in 2007. In a prior life (before grad school), he was a structured equity products trader at Goldman Sachs.
Brad Kenstler
Data Scientist
AWS
Brad Kenstler is a Data Scientist at AWS Deep Learning. As part of the Amazon Machine Learning Solutions Lab, he helps AWS customers accelerate their adoption of ML in their own products and processes. Brad’s primary interest is the intersection of Deep Learning and Computer Vision, particularly in the use-case of semantic segmentation. In the past Brad has interned with fast.ai as part of their popular Deep Learning MOOC, and maintains a passion for curating educational content & tutorials to this day.
Chris Fregly
Founder
PipelineAI
Chris Fregly is Founder and Applied AI Engineer at PipelineAI, a Real-Time Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production with Kubernetes and GPUs." Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
Faisal Siddiqi
Engineering Manager
Netflix
Faisal Siddiqi is an experienced technology leader with a strong focus on growing and retaining highly performant teams. Currently he leads Personalization Infrastructure for Netflix where his teams are focussed on accelerating innovation for Netflix Recommendations and Content Promotions by building highly scalable Machine Learning infrastructure. Prior to Netflix, Faisal built the platform engineering organization at streaming QoE startup Conviva where his teams deployed the first industry use of Apache Spark and scaled the multi-tenant real-time analytics platform to millions of concurrent viewers, an industry record at the time. Faisal has held Engineering Management and Research positions at Avaya, RouteScience, and Stanford's SLAC National Lab. Faisal has a Masters in Electrical Engineering from Stanford University where his research interests were in the fields of networking and distributed systems.
Adrian Kaehler
CEO
Giant.AI
Dr. Adrian Kaehler is a recognized expert and inventor in numerous advanced technology domains. Throughout his career, his primary focus has been on intellectual and practical leadership for complex technology innovation efforts for both privately or publicly held companies and enterprises, as well as for the many commercial and government institutions he advises. At this time, Adrian is a start-up founder and entrepreneur in Silicon Valley. His fields of expertise include robotics, deep learning, artificial intelligence, machine learning, physics, electrical engineering, computer algorithms, machine vision, biometrics, computer games, system engineering, human machine interface, numerical programming, and design. He is the author of numerous papers and over 30 patents in these and other subjects, as well as two a best- selling books on computer vision.
Francesco Mosconi
Data Scientist
Catalit
Francesco Mosconi is founder and data scientist at Catalit and Data Weekends. He was formerly co-founder and Chief Data Officer at Spire, a company backed by Y-Combinator, that invented the first consumer wearable device capable of continuously tracking respiration and physical activity. Machine Learning and python expert, he also served as Data Science lead instructor at General Assembly and at The Data Incubator. In the past he worked with startups, large companies and investment groups helping them with their data needs and unlocking value from their data.
Lukasz Kaiser
Sr. Research Scientist
Google Brain
Lukasz joined Google in 2013 and is currently a senior Research Scientist in the Google Brain Team in Mountain View, where he works on fundamental aspects of deep learning and natural language processing. He has co-designed state-of-the-art
neural models for machine translation, parsing and other algorithmic and generative tasks and co-authored the TensorFlow system and the Tensor2Tensor library. Before joining Google, Lukasz was a tenured researcher at University Paris Diderot and worked on logic and automata theory. He received his PhD from RWTH Aachen University in 2008 and his MSc from the University of Wroclaw, Poland.
Cami Williams
Sr. Engineer
Amazon
Cami works as a Senior Evangelist on Alexa for Amazon based in Seattle focused on Alexa Games. Since starting at Amazon in September 2017, Cami has spoken on behalf of Alexa at various conferences and workshops, including the Grace Hopper Celebration for Women in Computing, reInvent, Consumer Electronics Show, and Game Developers Conference. Cami is proud to be an advocate for diversity and inclusion in the tech industry. As a representative from the community she has spoken at over 40 different hackathons and events, most notably the GHC, the White House Tech Inclusion Summit, SXSWedu, ATECH Conference, and Google Women Engineers Conference. During her free time, Cami loves to play board games with friends, build mechanical keyboards, and quote the Office.
Jimmy Su
Head of Security Research
JD.COM
Dr. Jimmy Su leads the JD security research center in Silicon Valley. He joined JD in January 2017. Before joining JD, he was the director of advanced threat research at FireEye Labs. He led the research and development of multiple world leading security products at FireEye, including network security, email security, mobile security, fraud detection, and end-point security. He led a global team including members from the United States, Pakistan, and Singapore from research to product releases on the FireEye’s first machine learning based malware similarity analysis Cloud platform. This key technology advance was released on all core FireEye products including network security, email security, and mobile security. He won the Q2 2016 FireEye innovation award for his seminal work on similarity analysis. He earned his PhD degree in Computer Science at the University of California, Berkeley in 2010. After his graduation, he joined Professor Dawn Song’s team as a post doc focusing on similarity analysis of x86 and Android applications. In 2011, he joined Professor Song in the mobile security startup Ensighta, leading the research and development of the automatic malware analysis platform. Ensighta was acquired by FireEye in December of 2012. He joined FireEye through the acquisition. JD security research center in Silicon Valley focuses on these seven areas: account security, APT detection, bot detection, data security, AI applications in security, Big Data applications in security, and IoT security.
Venue: Plug and play Tech center, 440 N Wolfe Rd, Sunnyvale, CA 94085
8:30am-11:30am
Abstract: Recently, advances in computer vision have been dominated by deep learning models and con- volutional neural networks (CNN's). The well-known problem of image classification has been more or less solved by CNN's such as ResNet, and although challenges remain, the blueprint for training models that surpass human performance is well-defined.
Of greater interest is the problem of localization; finding the location of an object within an im- age in addition to determining what that object is. Models that are capable of performing this task are useful in a variety of scenarios, from object detection for self-driving cars to identifying cancerous material in CT scans. However, unlike image classification, there is no general consen- sus on the best practices for building such models, and this remains a field of active research.
In general, localization tasks fall into one of two categories: object detection and semantic seg- mentation. Object detection typically involves locating the general region or bounding box of a potential object, and then classifying said region. Semantic segmentation can be described as pixel-level classification, where the goal is to predict a pixel-by-pixel mask of each class of object.
In this workshop, we will introduce the tasks of object detection and semantic segmentation, as we as discuss how each problem is framed and represented in terms of data. Next, we will review the recent progress and advancements in both fields including: R-CNN, Fast R-CNN, Faster R- CNN, and SSD for object detection; FCN, U-Net, and E-Net for semantic segmentation. Finally, we'll take a deep dive into SageMaker, demonstrating how the platform simplifies training these models for applicable use-cases.
In this workshop, participants will learn about the following two localization techniques:
Object detection
Semantic segmentation
For each technique, participants will go through the following:
Rigorous definition of the task
An overview of recent progress / trajectory in the field
A walk-though for training a model on SageMaker
Prerequisite:This talk is geared to a level 300-400 crowd. At a minimum, participants should be comfort- able with Convolutional Neural Networks (CNN's), which assumes strong understanding of Deep Learning fundamentals. Computer vision techniques such as image processing are also desired.
Should have/create an AWS account with GPU access.
11:30am-12:30pm
Lunch
12:30pm-2:30pm
Abstract: In this hands-on workshop, developers will build a chatbot using Watson AI. Developers will learn how to use the Watson Assistant and Watson Tone Analyzer services. Developers will test whether the chatbot exhibit intelligent behavior (Turing test). This is a fun, hands-on session you don't want to miss!
2:50pm-5:10pm
Abstract:Keras is powerful and simple to use python library for Deep Learning. It integrates well with tensorflow and it is great for learning, prototyping and rapid development. In this workshop we introduce deep learning with Keras and cover fully connected, convolutional and recurrent neural networks. We apply them to a variety of common problems in industry including:
churn prediction.
image classification.
time series forecasting.
The workshop is hands on.
Instructor:Francesco Mosconi is founder and data scientist at Catalit and Data Weekends. He was formerly co-founder and Chief Data Officer at Spire, a company backed by Y-Combinator, that invented the first consumer wearable device capable of continuously tracking respiration and physical activity. Machine Learning and python expert, he also served as Data Science lead instructor at General Assembly and at The Data Incubator. In the past he worked with startups, large companies and investment groups helping them with their data needs and unlocking value from their data.
April 11th Wednesday: Hands-on Workshop Day 2
Venue: Plug and play Tech center, 440 N Wolfe Rd, Sunnyvale, CA 94085
9:00am-12:00pm
Abstract:Recurrent Neural Networks power deep learning systems that deal with sequences of data: weather data series, sales, sensor measurements, market data, and also natural language. They are a powerful tool you can add to your ML engineering toolbox and this session will help you do so. We will cover the basic principles behind recurrent neural networks and then apply them to various datasets. Along the way, we will share engineering best practices and practical Tensorflow tips and tricks for building and optimizing RNNs so that you can use them in your own projects. Machine learning beginners are welcome although it is recommended to follow a neural network crash course beforehand (you can start here: https://youtu.be/u4alGiomYP4, and then continue to RNNs: https://youtu.be/fTUwdXUFfI8)
Installation:Install Python3 then pip(3)-install jupyter, tensorflow and matplotlib;
(on Windows, Anaconda is recommended).
12:00pm-1:30pm
Lunch
1:30pm-4:30pm
Abstract: A deep dive into deep reinforcement learning and get hands on experience on applying deep RL to games.
Agenda:
The basics of reinfrocement learning.
Q-learning
Introduction to deep reinforcement learning
Policy gradients.
Actor-critic algorithms.
Introduction of an extensive, lightweight and flexible platform for deep RL & designing AI agents ( http://github.com/facebookresearch/ELF)
4:30-5:30pm
Happy hours and graduation party of two days training
Abstract: For the past six years, the Google Brain team (g.co/brain) has conducted research on difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. Our group has open-sourced the TensorFlow system (tensorflow.org), a widely popular system designed to easily express machine learning ideas, and to quickly train, evaluate and deploy machine learning systems. In this talk, I'll highlight some of the research and computer systems work we've done and the progress we’ve made in tackling problems in a wide variety of areas. This talk describes joint work with many people at Google..
9:50am
Abstract: to be update
10:35am
Coffee break and networking
11:00am
Abstract: to be update
11:45am
Lunch break, Networking
1:00-1:50pm
(Room 201)
Abstract: The research achievements in object classification, detection, segmentation are accompanied by a steep rise of Computer Vision adoption in industry. These progress open the doors to a whole new world of possibilities. As industrial applications mature, the challenges slowly shift towards challenges in limitation of memory, compute and speed. These new important metrics raise from the needs of running computer vision algorithms on mobile or embedded devices and serving large scale audience on backend services. This talk will discuss efficient neural networks with high performance model architectures.
(Room 206)
Abstract: Neural networks for sequence generation have a long history, from simple recurrent neural networks (RNNs) used for language modeling, through LSTMs applied to translation to many newer models that utilize convolutions and attention mechanisms and are widely applied in natural language processing. We give an overview of these developments with focus on recent methods. We show how attention and convolutions brought training time down by orders of magnitude while allowing to model sequences with dozens of thousands of elements, in contrast to just dozens for early RNNs. These developments allow to apply autoregressive models not just to sentences but also to whole articles and images. We present recent results on generating entire Wikipedia articles and on generating images, where we outperform all current GAN architectures. We highlight a single model that works for text, audio and images and brings us closer to the goal of generating anything. We also talk about our efforts to open-source these models to make generative deep learning broadly accessible.
(Room 207)
Abstract: Stitch Fix systems, composed of machines and human experts, need to recommend the maternity line when she says she’s in her ‘third trimester’, identify a medical professional when she writes that she ‘used to wear scrubs to work’, and distill ‘taking a trip’ into a Fix for vacation clothing. This talk is about how to form bridges between machine learning models and human experts by post-processing your model into something explainable (via t-SNE, the k-SVD and lda2vec) and by building models that can tell you what they *don't* know via Bayesian variational methods. Throughout, I'll share simple examples that extend word2vec, factorization machines and t-SNE to make Bayesian and interpretable versions. At the end, we'll extend t-SNE to make a hybrid "Poincaré SNE" that allows us to model hierarchical relationships for the first time
(Room 212)
Abstract: Whether automating digital customer interactions using bots, predicting the best sales and marketing targets, or reducing waste in logistics and manufacturing - Artificial Intelligence improves business operations once deployed. Companies need to redefine themselves by building models and learning from data.
Companies often start with building single models, and quickly realize that this is only the first step. Making predictions available at the right time, in the right context to drive the action requires significant effort: connecting data streams, extracting features, training models and sending predictions to a front-end application. Beyond all the engineering, it means infrastructure and alerting need to be in place, along with an ability to experiment and iterate. Once companies deploy one such model, replicating this success is even more difficult. Generalizing the methods and avoiding duplicated effort is necessary if the desire is to go beyond a handful of additional models. Even so, without planning this means taking one-off approaches to painstakingly handle increased data volumes and variety, new modeling approaches, different applications, etc. Scaling to 100s becomes improbable.
At Salesforce we need to surpass hundreds of thousands. For this we built the Einstein Platform. With its automation of Artificial Intelligence and services built for handling 1000s of customers, each with multiple models. From data ingestion, automated machine learning, experimentation frameworks, and instrumentation and intelligent monitoring and alerting make it possible to serve the varied needs of many different businesses. In this talk we will cover the nuts and bolts of the system, and share how we learned to solve for scale and variability with a fully operational Machine Learning platform.
2:00-2:50pm
(Room 201)
Abstract: State-of-the-art algorithms for applications like face recognition, object identification, and tracking utilize deep learning-based models for inference. Edge-based systems like security cameras and self-driving cars necessarily need to make use of deep learning in order to go beyond the minimum viable product. However, the core deciding factors for such edge-based systems are power, performance, and cost, as these devices possess limited bandwidth, have zero latency tolerance, and are constrained by intense privacy issues. The situation is further exacerbated by the fact that deep learning algorithms require computation of the order of teraops for a single inference at test time, translating to a few seconds per inference for some of the more complex networks. Such high latencies are not practical for edge devices, which typically need real-time response with zero latency. Additionally, deep learning solutions are extremely compute intensive, resulting in edge devices not being able to afford deep learning inference.
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. Siddha demonstrates how Deep Vision’s solutions offer better performance and higher accuracy and shares a classified secret to achieving higher accuracy with a smaller network, as well as how to optimize for information density.
(Room 206)
Abstract: Data science and machine learning are critical enabling factors for data-driven organizations. There has been an exponential rise of expectations put on engineering organizations to meet the demand to develop and scale machine learning capabilities. A machine learning platform is not just the sum of it’s parts; the key is how it supports the model life-cycle end-to-end. This includes: data discovery, feature engineering, iterative model development, model training, model scoring (batch and online). The management of artifacts, their associations and deployment across various platform components is vital.
· While there are a number of mature technologies that support each phase of these life-cycle capabilities, there are limited solutions available that tie these components together into a cohesive Machine Learning platform. To support the life-cycle of a model, we need to be able to manage the various ML related artifacts, their associations and automate deployment. A life-cycle management service built for this purpose should be leveraged for storage, versioning, visualizing(including associations) and deployment of artifacts. Below are several requirements for this service: The platform should support model development in different programming languages, language and package versions should be configured specific to a model.
· Having the model environment follow the model through the life-cycle is important, for the environment should be externalized, associated and deployed together with a model.
· Various artifacts and platforms(Connection among these components is required):
Data and datasets: including source data and feature data, training datasets and scoring result-sets
Code: including notebook code, model code, deployment code
Model specific environments: should be externalized, associated and deployed with a model
Platforms: including developing and training platform, batch and online scoring platform
Hope Wang describes how her team at Intuit is managing the machine learning life-cycle, how different components associate and interact with each other, and how to execute in a production environment. Hope takes an example of how an integrated process was developed for data engineers and data scientists to manage the entire life-cycle of a model from ideation through development, training and ultimately scoring.
(Room 207)
Abstract: to be updated.
(Room 212)
Abstract: The road to take Machine Learning and Deep Learning applications to production and scaling them is hard and time consuming. In this session we’ll explore how Amazon SageMaker simplifies the end to end machine learning process. Building and debugging Deep Learning models has been cumbersome see how Gluon an imperative interface for Apache MXNet allows to iterate faster. In addition we’ll dive in to the architecture of Amazon SageMaker and walk through examples of SageMaker training and deployment with Apache MXNet and Gluon.
2:50-3:20pm
Networking Break
3:20-4:10pm
(Room 201)
Abstract: I will summarize some recent work from my group at Google related to visual scene understanding and "grounded" language understanding, including:
Our DeepLab system for semantic segmentation (PAMI'17, 2nd place COCO-Stuff 2017) [1].
Our object detection system (CVPR ‘17, 1st place COCO-Detection 2016) [2].
Our person detection/pose estimation system [3] (CVPR'17, 4th place COCO-Keypoints 2017)
Optimizing semantic metrics for image captioning using RL (ICCV'17, 2nd place on COCO-captions 2017) [6]
Generative models of images and text (ICLR'18). [7]
I will explain how each of these pieces can be combined to develop systems that can better understand images and words
Reference:
[1] https://arxiv.org/abs/1606.00915
[2] https://arxiv.org/abs/1611.10012
[3] https://arxiv.org/abs/1701.01779
[4] https://arxiv.org/abs/1511.02283
[5] https://arxiv.org/abs/1701.02870
[6] https://arxiv.org/abs/1612.00370
[7] https://arxiv.org/abs/1705.10762
(Room 206)
Abstract: Airbnb has had over 260 million guest arrivals over all time and is growing rapidly. Naturally, this exponential growth is a challenge to deal with from the perspective of the sheer amount of user-generated text created. Every listing contains text written by a host (listing descriptions) and text written by guests (reviews). Customer service issues come in over chat and email, and guests and hosts exchange hundreds of thousands of messages every day. This talk will present an overview of some of the work we have done in this area and will also preview a lot of the work that still needs to be done.
(Room 207)
Abstract: Over the past few years, deep learning training has matured significantly, with well-known and standard solutions being applied to numerous scenarios. In contrast, deep learning inference is still largely implemented in ad-hoc ways, where existing solutions do not yet offer the required performance, efficiency, scalability and extendibility across hardware, frameworks, and usage scenarios for large-scale services. Here at Microsoft, we present the Deep Learning Inference Service, a cloud offering for ultra-fast and cost-efficient inference at scale. This distributed service handles several million production requests per second with single-digit millisecond latencies. We will go over the anatomy of the service and discuss topics such as:
Model acceleration via modern CPU features
Framework and hardware extensibility
Resource isolation in multi-tenant environments
Online/offline workload differentiation
Distributed batching and routing
Further, we will discuss how this service fits various needs of machine learning scientists, and performance requirements of site engineers. We will also dive into how it greatly boosts relevance for Microsoft Bing Search.
(Room 212)
Abstract: Data products are everywhere nowadays. It seems like even the most rudimentary products can be made glamorous by simply injecting some Machine Learning magic into the mix. This trend unsurprisingly correlates with the explosion of content that we consumers have to deal with on a daily basis. The huge amount of information that submerges us causes simultaneously a problem that we need to solve (retrieving relevant information efficiently), and the opportunity to solve this problem.
At Atlassian, we take this deluge of content that our users face very seriously. Indeed, if a Confluence page or a Jira ticket that isn’t discoverable, the information that it contains is lost forever. And this is a problem that needs to be solved for every single one of our products. How does a single team supports all the Machine Learning needs of many different product teams? This is precisely the purpose of Data Science as a Service. In this talk, I will discuss how the Search and Smarts Engineering team at Atlassian is building models to serve the majority of internal customers, and talk about the challenges of building a Data Science function as a Platform team
4:10-5:00pm
(Room 201)
Abstract: The practice of medicine involves diagnosis, treatment, and prevention of diseases. Recent technological breakthroughs have made little dent to the centuries-old system of practicing medicine: complex diagnostic decisions are still mostly dependent on “educated” work-ups of the doctors, and rely on somewhat outdated tools and incomplete data. In the meantime, AI is emerging as a truly world-changing revolution and it is significantly impacting every industry and scientific field.
With a growing research community as well as tech companies working on applying AI advances to medicine, the hope for healthcare renaissance is definitely not lost. In this talk, we will review some of the recent AI advances, with an emphasis on ML-driven medicine. We will discuss using AI for aiding medical decision including language understanding, medical knowledge base construction and diagnosis systems. We will discuss the importance of personalized medicine that takes into account not only the user, but also the context, and other metadata. We will also highlight challenges in designing ML-based medical systems that are accurate, but at the same time engaging and trustworthy for the user.
(Room 206)
Abstract: Any large-scale Machine Learning (ML) application requires substantial infrastructure and systems engineering elements that are critical to its success. At Netflix, we conduct research in Recommendations and Personalization to optimize for member joy as our increasingly global membership discovers engaging content created by storytellers all over the world. To handle this challenge, our systems have scaled to the diverse personal preferences of the 117+ million Netflix members. Over the years, as the sophistication and breadth of ML algorithms and applications in the Personalization space has grown, so too has our need for robust, flexible and extensible infrastructure. In this talk, we will explore some of the design abstractions we use and lessons learned from building a paved path for ML systems in the Personalization domain. We will discuss details of a handful of generic libraries and systems we have built for various elements of ML infrastructure such as preparing training data, adhoc exploration, model development, and disciplined online deployments. We will also touch upon future directions to meet the evolving infrastructure needs of our use cases.
(Room 207)
Abstract: Advanced RNN architectures are powering real-world applications such as comment moderation or language translation. This session is a deep dive into the most recent advances in the field, such as attention mechanisms, and gives you tips, engineering best practices and pointers to apply in your own projects. No PhD required.
Post-Conference Session
5:00-8:00pm
AI Night: games, topics, discussions on AI
Dinner reception with speakers and invited guests.
Abstract: Artificial Intelligence (AI) is behind practically every product experience at LinkedIn. From ranking the member’s feed to recommending new jobs, AI is used to fulfill our mission to connect the world’s professionals to make them more productive and successful. In this talk, I will provide an overview of the lessons learned and approaches we have developed to address this challenging problem, including scaling to large problem sizes, handling multiple conflicting objectives functions, and our progress toward using machine learning to optimizing the LinkedIn product ecosystem more holistically
9:50am
Abstract: Deep Reinforcement Learning (DRL) has made strong progress in many tasks that are traditionally considered to be difficult, such as complete information games, navigation, architecture search, etc. Although the basic principle of DRL is quite simple and straightforward, to make it work often requires substantially more samples with more computational resource, compared to traditional supervised training. This task presents our recent open-sourced works: efficient, lightweight and flexible frameworks and diverse 3D environments, to facilitate DRL research. Based on these, we train agents in Real-time Strategy games and navigations, and show interesting behaviors using only a small amount of resource
10:35am
Coffee break and networking
11:00am
Abstract: Over the last few years TensorFlow has enabled significant advances in deep learning research. In this talk I will discuss how the flexibility of TensorFlow makes it a great tool for research from workstations to supercomputer scale. The impact of TensorFlow and deep learning has come not just from the exciting research, but also the design of providing a library that can easily be deployed in products across a wide variety of platforms - from large scale data centers to mobile phones and edge devices. The talk will also cover some of the recent changes in TensorFlow and provide a glimpse of where it is headed
11:45am
Lunch break and networking
1:00-1:50pm
(Room 201)
Abstract: to be update
(Room 206)
Abstract: This morning I had the conversation with my vacuum cleaning robot" - in this session, Marek Sadowski, an entrepreneur and a developer advocate, will demonstrate the power of voice UI, mobile and AI services from the cloud platform to securely connect and manage devices, analyze social network data, and apply AI to add human-like interaction to lightweight IoT products.
Marek will show how you can quickly and securely turn a simple idea into reality by transforming a regular robot vacuum into a AI-enabled smart and chatty device, that would analyze and react to pictures, and could be turned into the media information center with an opinion on virtually any subject based on the social network sentiment digesting with cognitive services.
(Room 207)
Abstract: to be update
(Room 212)
Abstract: GoPro’s camera, drone, mobile devices as well as web, desktop applications are generating billions of event logs. The analytics metrics and insights that inform product, engineering, and marketing team decisions need to be distributed quickly and efficiently. We need to visualize the metrics to find the trends or anomalies.
While building up the features store for machine, we need to visualize the features, Google Facets is an excellent project for visualizing features. But can we visualize larger feature dataset?
These are issues we encounter at GoPro as part of the Data Platform evolution. In this talk, we will discuss few of the progress we made at GoPro. We will talk about how to use Slack + Plot.ly to delivery analytics metrics and visualization. And we will also discuss our work to visualize large feature set using Google Facets with Apache Spark.
2:00-2:50pm
(Room 201)
Abstract: to be update
(Room 206)
Abstract: I will talk about how sequence modeling can be applied in many different areas, more specifically I will talk about speech recognition, machine translation and speech synthesis by using sequence modeling
(Room 207)
Abstract: Machine Learning (ML) software differs from traditional software in the sense that outcomes are not based on a set of hand-coded rules and hence not easily predictable. The behavior of such software changes over time based on data and feedback loops. At Salesforce Einstein, we care deeply about building trust and confidence in such intelligent software programs. Why does a particular email have a higher likelihood of being opened than another? What are the shapes and patterns in the dataset, which lead to certain predictions? And can such insights be actionable?
As machine learning pervades every software vertical, and is increasingly used to automate decisions, model interpretability becomes an integral part of the ML pipeline, and can no longer be an afterthought. In the real world, the demand for being able to explain a model is rapidly gaining on model accuracy and other model evaluation metrics.
This talk will discuss the steps taken at Salesforce Einstein towards making machine learning transparent and less of a black box. We will explain how interpretability fits into the ML data pipeline, what we learned trying different approaches and how it has helped drive wider adoption of ML software.
(Room 212)
Abstract: As the growth in the e-commerce sector accelerates, it has created a fertile environment for the Dark Web to develop an increasingly sophisticated and matured ecosystem for fraud. In this talk, we will expose the techniques and methods for the leading e-commerce fraud tools in the Chinese market. These tools have a variety of functionalities, including:
1. bot registration
2. bot ordering
3. captcha solving by AI and humans
4. real-time price monitoring
5. unblacklist an account
2:50-3:10pm
Networking Break
3:10-4:00pm
(Room 201)
Abstract: Applying my Netflix experience to a real-world problem in the ML and AI world, I will demonstrate a full-featured, open-source, end-to-end TensorFlow Model Training and Deployment System using the latest advancements with TensorFlow, Kubernetes, OpenFaaS, GPUs, and PipelineAI.
In addition to training and hyper-parameter tuning, our model deployment pipeline will include continuous canary deployments of our TensorFlow Models into a live, hybrid-cloud production environment.
This is the holy grail of data science - rapid and safe experiments of ML / AI models directly in production.
Following the famous Netflix Culture that encourages "Freedom and Responsibility", I use this talk to demonstrate how Data Scientists can use PipelineAI to safely deploy their ML / AI pipelines into production using live data.
Offline, batch training and validation is for the slow and weak. Online, real-time training and validation on live production data is for the fast and strong.
Learn to be fast and strong by attending this talk!
(Room 206)
Abstract: This talk offers a quick deep dive into the technical complexities of achieving personable conversation in technology. It kicks off with a high-level overview of the history of design in technology, highlighting what we have learned over the years in developing for a screen. Then, we will establish new best practices for a voice-first design using the Alexa Skills Kit, contrasting with GUI design principles. We dive into some of the complexities of accepting conversational nuances and interpreting them in the ASK SDK. In the end, we hope to offer a new perspective for technical design and implementation, as well as spark interest in joining the Alexa community to help revolutionize voice technology.
(Room 207)
Abstract: Analytic applications rely on data and most of time and resources of machine learning project were spent on bringing together existing data sources and when appropriate, transform , enrich them with other data sets. I will explore the architecture and design patterns for a modern data pipeline framework and share the observations of technology assessment between Apache Nifi and StreamSets.
4:00-5:00pm
happy hours, open discussion, networking
*speakers and schedules are subject to change.
Why Attend
Speakers
50+ tech lead speakers from Engineering Teams at Microsoft, Google, Amazon, Facebook, Uber, Linkedin, Pinterest, Nvidia, Twitter, and more.
Topics
60+ deep dive tech topics and practicial experiences in machine learning, deep learning, computer vision, speech reconginition, NLP, data science and analytics. specially geared to tech engineers who want to grasp AI tech applied to their daily project.
Networking
Connect with 1000+ tech engineers, developers, data scientists; learn from peers, small-group discussions, office-hour, and lunch with speakers, happy hours, AI job fair.
Continuous Learning
Continue to learn and practice AI post conference, free access to AI learning materials (latest AI news, tech articles/blogs, live tech talks, tutorial vidoes, and hands-on workshop/codelabs, etc..). join free online learning AI group with 300+ speakers, 50,000+ tech engineers. Join.
Exploration
While immersed with 4 days extensive learning, you will also have opportunity in exploring cool AI products, enjoying the breakfast, lunch, coffee, wifi. also lucky draw with grand prizes.
Sponsors
Partners
Venue
DATE:
April 10-13th, 2018
VENUE:
Santa Clara Convention Center
5001 Great America Parkway Santa Clara, CA 95054
Contact
AICamp
Online learning and practicing AI with developers globally