Presented By O’Reilly and Intel AI
Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK
 
King's Suite - Balmoral
11:05 How to build privacy and security into deep learning models Yishay Carmiel (IntelligentWire)
11:55 Federated learning Ryan Micallef (Cloudera Fast Forward Labs)
13:45 Sense-Infer-Act-Learn: A model for trustworthy AI Rupert Steffner (WUNDER)
14:35 Designing user interfaces for AI for unbiased decision making Rachel Bellamy (IBM Research), Casey Dugan (IBM Research)
16:50 AI for automation and influence in open science publishing Daniel Ecer (eLife Sciences Publications Ltd), Paul Shannon (eLife Sciences Publications Ltd)
King's Suite - Sandringham
11:05 Applied machine learning at Facebook: An infrastructure perspective Yangqing Jia (Alibaba Group), Dmytro Dzhulgakov (Facebook)
11:55 On the road to artificial general intelligence Danny Lange (Unity Technologies)
13:45 PyTorch 1.0: Bringing research and production together Dmytro Dzhulgakov (Facebook)
14:35 Multitask networks on mobile environments Bruno Fernandez-Ruiz (Nexar)
Buckingham Room - Palace Suite
11:05 Building AI with TensorFlow: An overview (sponsored by Google) Sandeep Gupta (Google), Edd Wilder-James (Google)
13:45 Tensor2Tensor (sponsored by Google) Ryan Sepassi (Google)
14:35 Frontiers of TensorFlow: Mathematics and music (sponsored by Google) Joshua Dillon (Google Research), Wolff Dobson (Google)
Blenheim Room - Palace Suite
Windsor Suite
11:05 Scaling machine intelligence with IPUs Nigel Toon (Graphcore)
Park Suite
11:05 A day in the life of a data scientist in an AI company Francesca Lazzeri (Microsoft), Jaya Susan Mathew (Microsoft)
11:55 Beyond the contract: Effective cross-sector collaboration and the Turing-HSBC partnership Christine Foster (The Alan Turing Institute), Rakshit Kapoor (HSBC)
13:45 AI and financial crime Martin Goodson (Evolution AI), Mark Qualter (RBS)
16:00 The future of conversational UI Alice Zimmermann (Google)
Westminster Suite
11:05 TonY: Native support of TensorFlow on Hadoop Jonathan Hung (LinkedIn), Keqiu Hu (LinkedIn), Anthony Hsu (LinkedIn)
13:45 Scaling deep learning on AWS using C5 instances with MXNet, TensorFlow, and BigDL: From the edge to the cloud GAURAV KAUL (Amazon Web Services), Suneel Marthi (Amazon Web Services), Grigori Fursin (dividiti)
16:50 Reinforcement Learning Coach Gal Novik (Intel AI)
Hilton Meeting Room 3-6
11:05 Trust and transparency of AI for the enterprise (sponsored by IBM Watson) Ruchir Puri (IBM), Hilary Kerner (Vice President, IBM Watson Marketing)
14:35 The AI in fail (sponsored by Teradata) Chris Hillman (Teradata)
16:00 Unsupervised ML and fraud detection with deep neural networks Giorgia Fortuna (Machine Learning Reply)
16:50
King's Suite
9:00 Wednesday opening remarks Ben Lorica (O'Reilly), Roger Chen (Computable)
9:20 The state of automation technologies Ben Lorica (O'Reilly), Roger Chen (Computable)
9:35 Why we built a self-writing Wikipedia Amy Heineike (Primer)
10:00 AI for a better world Ashok Srivastava (Intuit)
10:20 Rethinking software engineering in the AI era Yangqing Jia (Alibaba Group)
19:00 AI at Night | Room: Heist Bank
10:35 Morning break - sponsored by Amazon Web Services | Room: Sponsor Pavilion
12:35 Lunch - sponsored by IBM Watson Wednesday Topic Tables at Lunch | Room: Sponsor Pavilion
12:35 Wednesday Business Summit Lunch | Room: Thames
15:15 Afternoon break | Room: Sponsor Pavilion
17:30 Attendee Reception | Room: Sponsor Pavilion
8:15 Speed Networking | Room: King's Suite foyer
11:05-11:45 (40m) Implementing AI Deep Learning models, Ethics, Privacy, and Security, Text, Language, and Speech
How to build privacy and security into deep learning models
Yishay Carmiel (IntelligentWire)
In recent years, there's been a quantum leap in the performance of AI, as deep learning made its mark in areas from speech recognition to machine translation and computer vision. However, as artificial intelligence becomes increasingly popular, data privacy issues also gain traction. Yishay Carmiel reviews these issues and explains how they impact the future of deep learning development.
11:55-12:35 (40m) Models and Methods Deep Learning models, Ethics, Privacy, and Security
Federated learning
Ryan Micallef (Cloudera Fast Forward Labs)
Imagine building a model whose training data is collected on edge devices such as cell phones or sensors. Each device collects data unlike any other, and the data cannot leave the device because of privacy concerns or unreliable network access. This challenging situation is known as federated learning. Ryan Micallef discusses the algorithmic solutions and the product opportunities.
13:45-14:25 (40m) Implementing AI Ethics, Privacy, and Security, Retail and e-commerce
Sense-Infer-Act-Learn: A model for trustworthy AI
Rupert Steffner (WUNDER)
The increase in automated decision making, along with doubts in the quality of algorithmic decisions, has driven demand for transparency and accountability in AI. Rupert Steffner explains why the shift from black box to white box is a great opportunity to build AI models that create trust with the user and shares Sense-Infer-Act-Learn, a logical AI execution model to enable a more trustworthy AI.
14:35-15:15 (40m) Interacting with AI Ethics, Privacy, and Security, Interfaces and UX
Designing user interfaces for AI for unbiased decision making
Rachel Bellamy (IBM Research), Casey Dugan (IBM Research)
Data bias is not only an AI problem; it's also a UI problem. Non-AI experts use custom application interfaces to help them make decisions based on predictions from machine learning models. These application interfaces need to be designed so that the decisions made are unbiased. Rachel Bellamy and Casey Dugan explain how to represent model predictions so that people can recognize if they are fair.
16:00-16:40 (40m) Implementing AI, Models and Methods Media, Marketing, Advertising, Text, Language, and Speech
Legal contract review by an artificial intelligence
Rahul Dodhia (Microsoft)
Artificial intelligence is mature enough to make substantial contributions to the legal industry. Rahul Dodhia offers an overview of an AI assistant that can perform routine tasks such as contract review and checking compliance with regulations at higher accuracy rates than legal professionals.
16:50-17:30 (40m) Implementing AI Computer Vision, Media, Marketing, Advertising, Text, Language, and Speech
AI for automation and influence in open science publishing
Daniel Ecer (eLife Sciences Publications Ltd), Paul Shannon (eLife Sciences Publications Ltd)
eLife’s mission is to accelerate discovery and encourage responsible behaviors in science. Daniel Ecer and Paul Shannon detail eLife’s journey in using NLP, computer vision, and similarity algorithms to find more diverse peer reviewers, apply semantics to archive content, automate the submission process, and find insights into the sentiment of scholarly content.
11:05-11:45 (40m) Implementing AI Deep Learning tools, Edge computing and Hardware
Applied machine learning at Facebook: An infrastructure perspective
Yangqing Jia (Alibaba Group), Dmytro Dzhulgakov (Facebook)
Machine learning sits at the core of many essential products and services at Facebook. Yangqing Jia and Dmytro Dzhulgakov offer an overview of the hardware and software infrastructure that supports machine learning at global scale.
11:55-12:35 (40m) Interacting with AI Media, Marketing, Advertising, Reinforcement Learning
On the road to artificial general intelligence
Danny Lange (Unity Technologies)
Danny Lange discusses the role of intelligence in biological evolution and learning and demonstrates why a game engine is the perfect virtual biodome for AI’s evolution. You'll discover how the scale and speed of simulations is changing the game of AI while learning about new developments in reinforcement learning.
13:45-14:25 (40m) Computer Vision, Deep Learning tools
PyTorch 1.0: Bringing research and production together
Dmytro Dzhulgakov (Facebook)
Dmytro Dzhulgakov explores PyTorch 1.0, from its start as a popular deep learning framework for flexible research to its evolution into an end-to-end platform for building and deploying AI models at production scale.
14:35-15:15 (40m) Implementing AI, Models and Methods Deep Learning models, Edge computing and Hardware
Multitask networks on mobile environments
Bruno Fernandez-Ruiz (Nexar)
Bruno Fernandez-Ruiz details a unified network that jointly performs various mission-critical tasks in real time on a mobile environment, within the context of driving. Along the way, he outlines the challenges that emerge when training a single mobile network for multiple tasks, such as object detection, object attributes recognition, classification, and tracking.
16:00-16:40 (40m) Models and Methods Computer Vision, Deep Learning models, Ethics, Privacy, and Security, Retail and e-commerce
How CLEVER is your neural network? Robustness evaluation against adversarial examples
Pin-Yu Chen (IBM Research AI)
Neural networks are particularly vulnerable to adversarial inputs. Carefully designed perturbations can lead a well-trained model to misbehave, raising new concerns about safety-critical and security-critical applications. Pin-Yu Chen offers an overview of CLEVER, a comprehensive robustness measure that can be used to assess the robustness of any neural network classifiers.
16:50-17:30 (40m) Impact of AI on Business and Society, Implementing AI Edge computing and Hardware
What is ML Ops? Solutions and best practices for applying DevOps to production ML services
Kaz Sato (Google)
Kaz Sato offers an overview of ML Ops (DevOps for ML), sharing solutions and best practices for bringing ML into production service. You'll learn how to combine Apache Airflow, Kubeflow, and cloud services to build a data pipeline for continuous training and validation, version control, scalable serving, and ongoing monitoring and alerting.
11:05-11:45 (40m) Sponsored, TensorFlow at AI
Building AI with TensorFlow: An overview (sponsored by Google)
Sandeep Gupta (Google), Edd Wilder-James (Google)
TensorFlow is one of the world’s biggest open source projects, and it continues to grow in adoption and functionality. Sandeep Gupta and Edd Wilder-James share major recent developments, highlight some future directions, and explain how you can become more involved in the TensorFlow community.
11:55-12:35 (40m) Sponsored, TensorFlow at AI
Ready, set, go: Using TensorFlow to prototype, train, and productionalize your models (sponsored by Google)
Amit Patankar (Google)
Building machine learning models is a multistage process. TensorFlow's high-level APIs make this process smooth and easy, whether you are starting small or going big. Amit Patankar walks you through building, training, and debugging a model and then exporting it for serving using these APIs.
13:45-14:25 (40m) Sponsored, TensorFlow at AI
Tensor2Tensor (sponsored by Google)
Ryan Sepassi (Google)
Ryan Sepassi offers an overview of Tensor2Tensor, an open source library of datasets and models and a framework for training, evaluation, and decoding, built on top of TensorFlow. Tensor2Tensor is actively used and maintained by scientists and engineers within Google Brain.
14:35-15:15 (40m) Sponsored, TensorFlow at AI
Frontiers of TensorFlow: Mathematics and music (sponsored by Google)
Joshua Dillon (Google Research), Wolff Dobson (Google)
Joshua Dillon and Wolff Dobson discuss core TensorFlow Probability (TFP) abstractions and demo some of TFP's modeling power and convenience. They also share some of the recent results from Project Magenta, a research project exploring the role of machine learning in the process of creating art and music.
16:00-16:40 (40m) Sponsored, TensorFlow at AI
Cloud AutoML: Customize machine learning models with your own data (sponsored by Google)
Lucio Floretta (Google Cloud)
Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs, leveraging Google’s state-of-the-art transfer learning and neural architecture search technology. Lucio Floretta demonstrates the power and ease of use of AutoML Vision, Translate, and Natural Language.
16:50-17:30 (40m) Sponsored, TensorFlow at AI
Pragmatic ML development with scikit-learn and TensorFlow using Google ML Engine (sponsored by Google)
Zack Akil (Google)
Zack Akil shares pragmatic techniques and useful tools that can help you avoid common pitfalls when building ML, including tools for notebook collaboration and version control that will help prevent you and your teammates from stepping on each others' toes as well as an iterative ML model development approach that will prevent your project from stagnating.
11:05-11:45 (40m) AI Business Summit AI in the Enterprise, Financial Services
Executive Briefing: Moving AI off your product roadmap and into your products
Ashok Srivastava (Intuit)
Ashok Srivastava explains how to make your organization AI ready, determine the right AI applications for your business and products, and accelerate your AI efforts with speed and scale.
11:55-12:35 (40m) AI Business Summit AI in the Enterprise
Executive Briefing: Best practices for human in the loop—The business case for active learning
Paco Nathan (derwen.ai)
Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data.
13:45-14:25 (40m) AI Business Summit, AI in the Enterprise AI in the Enterprise
Executive Briefing: How AI startup and VC investment drives enterprise AI innovation
Diego Saenz (Accenture)
What do the world's most innovative and fastest growing companies have in common? They are in industries with a high level of VC funding. Accenture has analyzed five years of VC investment data to discover the AI use cases and technologies that are attracting the most money and will drive enterprise AI innovation. Diego Saenz explains where the top 10 investors in AI are placing big bets.
14:35-15:15 (40m) AI Business Summit AI in the Enterprise
Executive Briefing: Organizational design for effective AI
Mariya Yao (Metamaven)
Executives are being asked to "innovate with AI,” but the barriers to successful adoption for most enterprises are organizational, not technical. Mariya Yao explains why effective application of AI requires extended interdisciplinary coordination between executive and functional teams, investments in retraining your workforce, and the cultivation of an open, experimental, data-driven culture.
16:00-16:40 (40m) AI Business Summit Computer Vision, Data Networks and Data Markets
Executive Briefing: How to augment sparse training sets with synthetic data
Daeil Kim (AI.Reverie)
Daeil Kim delineates the advantages of synthetic data and explains how to avoid traps that lead to dead zones and false positives. He also reviews work on simulations for synthetic data in application verticals in which it is traditionally difficult to manually acquire significant datasets.
16:50-17:30 (40m) AI Business Summit AI in the Enterprise
Executive Briefing: Who is going to make money in AI? Understanding the value chain of AI
Simon Greenman (Best Practice AI)
We're experiencing an AI gold rush. Tech giants, corporations, startups, and governments are investing billions, and headlines about AI have reached fever pitch. It's dizzying to keep track of the latest AI developments and claims. Join Simon Greenman to learn who can and who will make money in this gold rush—and who will become economic casualties along the way.
11:05-11:45 (40m) Implementing AI Edge computing and Hardware, Platforms and infrastructure
Scaling machine intelligence with IPUs
Nigel Toon (Graphcore)
Nigel Toon explains how scaling IPUs will increase the productivity of machine intelligence researchers everywhere. Join in to explore what can we do and expect from the field with vastly more compute.
11:55-12:35 (40m) Models and Methods Deep Learning models, Ethics, Privacy, and Security
Harden and improve your deep learning models with targeted ensembles
Alan Mosca (nPlan)
Alan Mosca shows how any deep learning model can be improved and made more secure with the use of targeted ensemble methods and other similar techniques and demonstrates how to use these techniques in the Toupee deep learning framework to create production-ready models.
13:45-14:25 (40m) Models and Methods Temporal data and time-series
Business forecasting using hybrid approach: A new forecasting method using deep learning and time series
Pasi Helenius (SAS), Larry Orimoloye (SAS)
Business forecasting generally employs machine learning methods for longer and nonlinear use cases and econometrics approaches for linear trends. Pasi Helenius and Larry Orimoloye outline a hybrid approach that combines deep learning and econometrics. This method is particularly useful in areas such as competitive event (CE) forecasting (e.g., in sports events political events).
14:35-15:15 (40m) Models and Methods Deep Learning models, Temporal data and time-series
Forecasting at Uber: Machine learning approaches
Andrea Pasqua (Uber)
Andrea Pasqua investigates the merits of using deep learning and other machine learning approaches in the area of forecasting and describes some of the machine learning approaches Uber uses to forecast time series of business relevance.
16:00-16:40 (40m) Implementing AI Deep Learning models, Financial Services, Temporal data and time-series
The use of recommender systems in the chief investment office: A case study
Gaurav Chakravorty (qplum)
Gaurav Chakravorty explains how recommender systems can be utilized for investment management and details how AI and deep learning are used in trading today.
16:50-17:30 (40m) Implementing AI Platforms and infrastructure, Retail and e-commerce, Text, Language, and Speech
Deprecating the state machine: Building conversational AI with the Rasa stack
Alan Nichol (Rasa)
Alan Nichol walks you through building fully machine learning-based voice and chatbots with the open source Rasa stack.
11:05-11:45 (40m) AI Business Summit, Impact of AI on Business and Society AI in the Enterprise
A day in the life of a data scientist in an AI company
Francesca Lazzeri (Microsoft), Jaya Susan Mathew (Microsoft)
With the growing buzz around data science, many professionals want to learn how to become a data scientist—the role Harvard Business Review called the "sexiest job of the 21st century." Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.
11:55-12:35 (40m) AI Business Summit, AI in the Enterprise Financial Services
Beyond the contract: Effective cross-sector collaboration and the Turing-HSBC partnership
Christine Foster (The Alan Turing Institute), Rakshit Kapoor (HSBC)
In 2016, the Alan Turing Institute, the UK’s new national institute for data science and AI, announced a funded strategic multiyear research partnership with HSBC. Christine Foster and Rakshit Kapoor share insights and use cases that emerged while making this ambitious and innovative cross-sector partnership work.
13:45-14:25 (40m) AI Business Summit, AI in the Enterprise, Impact of AI on Business and Society Financial Services
AI and financial crime
Martin Goodson (Evolution AI), Mark Qualter (RBS)
Martin Goodson and Mark St. John Qualter share the results of a yearlong feasibility study on the introduction of AI into the onboarding process at the Royal Bank of Scotland (RBS). Along the way, Martin and Mark share their experiences in translating this complex business process into a high-performance computational system.
14:35-15:15 (40m) AI Business Summit, AI in the Enterprise Financial Services, Retail and e-commerce
How AI is taking geospatial data from alternative to mainstream in finance
James Crawford (Orbital Insight)
By some estimates, soon it will require eight million people doing nothing but looking at satellite imagery 24/7 in order to ensure every photo taken on a daily basis is viewed. James Crawford explains how artificial intelligence solves this problem of scale, allowing us to accurately analyze reams of satellite imagery and detect patterns of socioeconomic change in a timely fashion.
16:00-16:40 (40m) AI Business Summit, Impact of AI on Business and Society Interfaces and UX
The future of conversational UI
Alice Zimmermann (Google)
Fueled by the growth of messaging apps, conversational interfaces are quickly becoming an essential component of every service and product. Join Alice Zimmermann to learn how Google approaches the emerging UX challenges in its conversational agent platform. Along the way, Alice discusses the opportunities in this space and the future of conversation agents.
16:50-17:30 (40m) AI in the Enterprise
An ecosystem analysis of the AI industry, using the case of autonomous driving
Weiyue Wu (University of Oxford)
Does good technology equal a good product? Not necessarily. Instead of taking only technology into account, you may need to deep dive into the AI ecosystem and look at other players and factors. Weiyue Wu explains how such analysis can help in predicting AI implementation schedules, prioritizing corporate tasks, and allocating resources efficiently.
11:05-11:45 (40m) Implementing AI Deep Learning tools, Platforms and infrastructure
TonY: Native support of TensorFlow on Hadoop
Jonathan Hung (LinkedIn), Keqiu Hu (LinkedIn), Anthony Hsu (LinkedIn)
Jonathan Hung, Keqiu Hu, and Anthony Hsu offer an overview of TensorFlow on YARN (TonY), a framework to natively run TensorFlow on Hadoop. TonY enables running TensorFlow distributed training as a new type of Hadoop application. Its native Hadoop connector, together with other features, aims to run TensorFlow jobs as reliably and flexibly as other first-class objects on Hadoop.
11:55-12:35 (40m) AI in the Enterprise Platforms and infrastructure
The OS for AI: How microservices and serverless enable the next generation of machine intelligence
Diego Oppenheimer (Algorithmia)
Diego Oppenheimer explains why machine learning is a natural fit for serverless computing, shares a general architecture for scalable ML, discusses issues he ran into when implementing on-demand scaling over GPU clusters at Algorithmia, and provides general solutions and a vision for the future of cloud-based ML.
13:45-14:25 (40m) Deep Learning tools, Edge computing and Hardware, Platforms and infrastructure
Scaling deep learning on AWS using C5 instances with MXNet, TensorFlow, and BigDL: From the edge to the cloud
GAURAV KAUL (Amazon Web Services), Suneel Marthi (Amazon Web Services), Grigori Fursin (dividiti)
Gaurav Kaul, Grigori Fursin, and Suneel Marthi share trade-offs and design choices that are applicable to deep learning models when training in the cloud, specifically focusing on convergence and numerical stability, which are very important for autonomous driving and medical imaging. They then demonstrate how to optimize cost, performance, and convergence using CPU spot instances in AWS.
14:35-15:15 (40m) Implementing AI Edge computing and Hardware
DragonFly+: An FPGA-based quad-camera visual SLAM system for autonomous vehicles
Shaoshan Liu (PerceptIn)
Shaoshan Liu explains how PerceptIn built the first FPGA-based computing system for autonomous driving.
16:00-16:40 (40m) Implementing AI, Interacting with AI Computer Vision, Deep Learning tools
Building a Pokédex to recognize Pokémon in real time using TensorFlow and object recognition
Anmol Jagetia (Media.net)
Machine learning and object recognition have matured to the point that exciting applications are now possible. Anmol Jagetia demonstrates how to create a Pokédex that uses a camera phone to recognize the Pokémon it's looking at in real time. You'll see how to gather data, prepare your dataset, tune models, and deploy it to a mobile device, using the same tech that is used in self-driving cars.
16:50-17:30 (40m) Reinforcement Learning
Reinforcement Learning Coach
Gal Novik (Intel AI)
Gal Novik offers an overview of Reinforcement Learning Coach, an open source Python library that models the interaction between an agent and an environment in a modular way, making it easy for researchers to implement new reinforcement learning algorithms and for data scientists to integrate additional simulation environments modeling their business problems.
11:05-11:45 (40m) Ethics, Privacy, and Security
Trust and transparency of AI for the enterprise (sponsored by IBM Watson)
Ruchir Puri (IBM), Hilary Kerner (Vice President, IBM Watson Marketing)
TBC
11:55-12:35 (40m) Sponsored
Advanced machine learning with Amazon SageMaker (sponsored by Amazon Web Services)
Julien Simon (Amazon Web Services)
Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Julien Simon offers a quick overview of SageMaker. Then, using Jupyter notebooks, he dives into the more advanced features of this service.
13:45-14:25 (40m)
The evolution of AI at the network edge: How silicon is paving a path for IoT innovation (sponsored by Intel AI)
Gary Brown (Intel)
Gary Brown explains how the use of AI in the IoT is leading to fascinating growth in various applications from industrial and medical to smart transportation and retail. Gary discusses Intel’s unique vantage point of the platforms paving the way for interesting new AI experiences. Along the way, he shares Intel’s latest IoT innovations.
14:35-15:15 (40m)
The AI in fail (sponsored by Teradata)
Chris Hillman (Teradata)
Christopher Hillman explores the reasons why AI projects fail and why in some cases this is good and in others bad. Chris then explains how to avoid making the same mistakes again.
16:00-16:40 (40m) AI in the Enterprise, Impact of AI on Business and Society Computer Vision, Financial Services
Unsupervised ML and fraud detection with deep neural networks
Giorgia Fortuna (Machine Learning Reply)
Many industries, including banking, financial sectors, and insurance, continuously face the problem of detecting fraudulent activities. Giorgia Fortuna explores state-of-the-art innovations in fraud detection and explains how unsupervised ML fits into the picture, focusing on signature checks and face recognition.
16:50-17:30 (40m)
Session
9:00-9:05 (5m)
Wednesday opening remarks
Ben Lorica (O'Reilly), Roger Chen (Computable)
Program cochairs Ben Lorica and Roger Chen open the first day of keynotes.
9:05-9:20 (15m)
AI in production: The droids you’re looking for
Jonathan Ballon (Intel)
Artificial intelligence in the future, at least represented in science fiction, can learn, interpret, and take action based on data analysis. AI in production is the present, a present that feels decidedly futuristic. Jonathan Ballon explains why Intel’s leading portfolio of AI and computer vision edge technology will drive advances that improve how we work and live.
9:20-9:30 (10m)
The state of automation technologies
Ben Lorica (O'Reilly), Roger Chen (Computable)
What technologies are ready for adoption, and how should companies and organizations evaluate automation technologies? Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.
9:30-9:35 (5m)
AI and machine learning at Amazon (sponsored by Amazon Web Services)
Ian Massingham (Amazon Web Services)
Ian Massingham discusses the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding, and explains how you can use easily accessible services from AWS to include AI features within your applications or build your own custom ML models for your own specific AI use cases.
9:35-9:50 (15m) Impact of AI on Business and Society Text, Language, and Speech
Why we built a self-writing Wikipedia
Amy Heineike (Primer)
Human-generated knowledge bases like Wikipedia have excellent precision but poor recall. Amy Heineike explains how Primer created a self-updating knowledge base that can track factual claims in unstructured text and describe what it learns in human-readable text.
9:50-10:00 (10m) Ethics, Privacy, and Security
Trust and transparency of AI for the enterprise (sponsored by IBM Watson)
Ruchir Puri (IBM)
TBC
10:00-10:15 (15m) AI in the Enterprise
AI for a better world
Ashok Srivastava (Intuit)
Industry buzz sometimes focuses on an AI future with dire unintended consequences for humanity. Ashok Srivastava draws on his cross-industry experience to paint an encouraging picture of how AI can solve big problems with people, data, and technology to benefit society.
10:20-10:35 (15m) Implementing AI Computer Vision, Edge computing and Hardware
Rethinking software engineering in the AI era
Yangqing Jia (Alibaba Group)
Yangqing Jia shares a series of examples to illustrate the uniqueness of AI software and its connections to conventional computer science wisdom. Yangqing then discusses future software engineering principles for AI compute.
19:00-21:00 (2h)
AI at Night
Join us at Heist Bank, the new playground for grown-ups that's just a short walk from the Hilton Metropole along the Grand Union Canal. Come meet fellow attendees for games, beverages, and wood-fired pizza.
10:35-11:05 (30m)
Break: Morning break - sponsored by Amazon Web Services
12:35-13:45 (1h 10m)
Wednesday Topic Tables at Lunch
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics.
12:35-13:45 (1h 10m)
Wednesday Business Summit Lunch
Join fellow executives, business leaders, and strategists for a networking lunch on Wednesday for AI Business Summit attendees and speakers.
15:15-16:00 (45m)
Break: Afternoon break
17:30-18:30 (1h)
Attendee Reception
Come enjoy delicious snacks and beverages with fellow AI Conference attendees, speakers, and sponsors at the Attendee Reception, happening immediately after the afternoon sessions on Tuesday.
8:15-8:45 (30m)
Speed Networking
Ready, set, network! Meet fellow attendees who are looking to connect at the AI Conference. We'll gather before Wednesday and Thursday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun.