Digital Developer Conference
Data & AI 2020
About the conference replays
This page provides replays of the 2020 Digital Developer Conference Data & AI. Replays are available free and on-demand.
Industry-recognized data and AI skills
This page provides replays of the 2020 Digital Developer Conference Data & AI. Replays are available free and on-demand.
4 Dedicated tracks
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AI in production
Clients share their challenges, and how they overcame them, through architectural and tooling solutions. Session topics include deploying models on the edge, cloud-based AI development environments, building personalized messaging at scale with AI, and the latest from IBM Research.
Day 1: November 10, 2020
Make the most out of the client experiences in this track. Take a minute for an overview of the track agenda and the value of open source in the cloud and on the edge designed for AI before you dive deeper.
At IBM Research, our innovation agenda in AI is focused on advancing the scientific and technical foundations of the field while also enabling enterprises to operationalize and deploy AI at scale. In this talk, we will provide a window into our work with examples of exciting projects and innovations from three areas, First, we will describe our work in scaling AI by driving automation into the entire lifecycle of building deploying and managing AI modes, all while addressing enterprise needs for security scale and compliance. Second, we will talk about our innovations in trustworthy AI and our open toolkits that address the key elements of fairness, robustness, explainability and transparency of AI models. Finally, we will talk about our leading edge innovations in natural language processing (NLP) and how we are bringing in advances from exciting grand challenges such as Project Debater to meet the requirements and needs of enterprise NLP.
Recent advancements in Cloud computing, which makes compute available for rent, Natural Language Processing via pre-trained language models such as Bi-directional Encoder Representations from Transformers (BERT) for language understanding, and Machine Learning via interpretable and explainable machine learning models are all making it possible to infuse AI into and to optimize traditional business processes. For example, AI can be put to use into Information Technology (IT) operations management process for increasing application availability, early problem detection and prediction, and efficient problem resolution and problem avoidance leading to self-aware, self-healing and self-managing operations processes.
In this talk, I will discuss the AI involved in preparing IT data for AI, the AI behind optimizing IT Operations Management, and the AI platform that powers the first two AIs. The AI in preparing IT data for AI includes entity extraction, entity resolution, and topology derivation. The AI pipelines in IT Operations Management include log and metric-based anomaly prediction, event grouping, fault localization, incident similarity, next best action prediction, and change risk prediction. The AI platform concerns include AI model training, feedback gathering, retraining, model monitoring and AI life cycle management. I will touch upon the differences between the role of a data scientist who builds the analytical pipelines in the product versus an operations person who manages the analytical pipelines in production in a customer environment and stress upon how AI platforms must be built differently for these two personas.
This session is for Data & AI product builders, data scientists and engineering managers who aim to build consumable AI-infused products.
Attendees will gain an understanding of the concerns and topics involved in building consumable, and production ready AI-infused solutions.
The session is covering a unique challenge where we had to help our client predict the impact of Media on in store and online sales (over 100 stores). We/WPP as a group own multiple data assets, those data assets are siloed given their nature and what this data is used for historically.
As a media agency we knew that we had to understand the non-media driver of sales in each geography using Wunderman Identity Network to which we layered in the media spend and engagement data as well as client sales data at a point of sales level.
The volume of data we have access is larger than any typical Media Mix Model which is usually limited to media and macro level data modeled at a weekly level.
We connected the data that was siloed and sitting with different business units, and we had a vision on how this data work together. We leverage the capabilities our IBM partners brought to the table from Data and AI portfolio and expertise to build a new product that will evolve our business and knowledge of consumers and their behaviors. This is approach allowed us to go to micro targeting and personalization with remaining complaining to laws and regulations around privacy.
The attendees will gain insight on the approach to define the ask and how we solved for the siloed data challenges.
David Bartram-ShawKarima Zmeril
This talk covers some of the lessons we've learned at Anaconda as we see businesses try to adopt open source data science. It's primarily for a business or manager audience, although we do touch on some deeper technical topics. Attendees will walk away with a deeper appreciation for the unique challenges of the data science revolution, and how to better think about some of the problems that arise between data scientists, software devs, and IT folks.
2020 marks the 400th anniversary of the original Mayflower voyage from Plymouth, UK to Plymouth, USA. To commemorate this anniversary, we have built the Mayflower Autonomous Ship - a 'Mayflower' for the 21st century that embodies the pioneering spirit of the original voyage but embraces ground-breaking technology encompassing design, propulsion and autonomous vehicle control. The ship is completely unmanned and will have limited contact as it traverses the unpredictable North Atlantic. The ship is controlled by an AI Captain, enabled by key IBM technology offerings such as Maximo Visual Insights, Edge, ODM, CPlex Optimizer, and the Weather Company. Join us in a presentation on the challenges of designing, building, and testing the ultimate edge device.
AI is going to bring huge benefits in terms of scientific progress, human wellbeing, economic value, and the possibility of finding solutions to major social and environmental problems. Supported by AI, we will be able to make more grounded decisions and to focus on the main values and goals of a decision process rather than on routine and repetitive tasks. However, such a powerful technology also raises some concerns, related for example to the black-box nature of some AI approaches, the possible discriminatory decisions that AI algorithms may recommend, and the accountability and responsibility when an AI system is involved in an undesirable outcome. Also, since many successful AI techniques rely on huge amounts of data, it is important to know how data are handled by AI systems and by those who produce them. These concerns are among the obstacles that hold AI back or that cause worry for current AI users, adopters, and policy makers. Without answers to these questions, many will not trust AI, and therefore will not fully adopt it nor get its positive impact. In this talk I will present the main issues around AI ethics, describe some of the proposed technical solutions, and mention some of the many initiatives that have been built in the past few years to address the technical, policy, and legal challenges, as well as the ethical concerns, around AI.
Take a dive into how we are applying AI to the world of ESPN Fantasy Football with 'Player Insights with Watson', and our new feature this year 'Trade Assistant with Watson'. You will learn about AI techniques that increase in complexity up to neural optimization approaches that are delivered 2 billion times per day to millions of users around the world. This session is for AI and sports enthusiasts who want to understand continuously on-demand and at-scale AI. We will introduce OpenShift, AI algorithms, NLP, Watson Discovery, OpenScale, Watson Machine Learning and many other exciting capabilities.
In March 2020, as COVID-19 exploded across the United States, a self-assembled team of IBM Data, AI, and Cloud developers self-assembled and built a scalable data lake of trusted COVID-19 incident data with granularity down to a U.S. county level. In just one week they went from an empty Slack channel to a deployed solution available to 300 million Weather.com users. This task was especially challenging because the data was fragmented across various state web sites, and often buried in PDF documents and PNG images for which we needed to use our Watson Natural Language Processing to accurately parse.
This is a story of climbing the AI ladder in a hyper-accelerated manner, and is an excellent story of business and technical agility, and breaking down organizational silos to achieve great user outcomes.
Bill HigginsJustin McCoyMisha Sulpovar
It is said that Software is eating the world! As the impact of AI on Society continues to expand, we will discuss the question “Can AI eat software?.” In some ways, that goal is a reality today. Thousands of lines of painstakingly written computer vision, speech and natural language understanding code can be replaced by automatically trained Data driven AI models. However, the goal of AI to truly understand software remains elusive. Just like human languages, programming languages have context. The meaning of a particular statement on a line actually is related to what occurs before, and deriving that context and making the translation, just like human languages, takes a lot of effort and time and resources. And the larger the program gets, the harder it gets to translate it over, even more so than human languages. While in human language, the context may be limited to that paragraph or maybe that particular document, here the context can actually relate to multiple libraries and other services that are related to that particular program. In this talk, we will discuss where we are in AI’s journey to ease our pain points as software developers.
Critical enterprise applications often run on IBM Z and can generate the majority of an organizations high-value data. This session will introduce you to Watson Machine Learning for z/OS, an end to end machine learning offering that delivers unique value to your mission critical applications with AI and machine learning initiatives. You get the opportunity to see demos of how you can build models on your platform of choice and readily deploy those models directly within your production applications on Z.
Big data and AI/ML infrastructure is quite challenging problem space, but we believe it is worth exploring. Let’s take a look at what roles Red Hat OpenShift can play in this space and how it can help developers and data scientists to achieve more by making sharing easier.
We’ll also discuss the challenge of enabling and managing specialized hardware across the technology stack and touch on why automation is key when we move our data exploration tools from our laptops to hybrid clouds. A brief peak into the Open Data Hub project will connect the dots from the talk and provide immediate next steps for interested participants in the audience.
In this session, I will cover our current journey to containerising Cora, the Bank‚Äôs conversational assistant. This involves moving from the existing Cloud Foundry centric method supported by manual delivery procedures to a Cloud First approach. I will explain how using Cloud Pak for Apps allows the Bank to leverage containerisation and CI/CD and remove the manual work, so that we can significantly accelerate benefit for customers. Also, I will talk through how implementing CI/CD in Urban Code Deploy (UCD), can put changes to AI and content in the hands of the business and significantly reduce the need for technology involvement in these types of changes. We will example the simplified deployment process and how it offers the business the agility to deliver change to customers quickly while maintaining the necessary level of governance and auditability.
Meet the speakers
Developer experts and leaders in artificial intelligence, machine learning, and data science have come together to share their expertise to elevate your skills.
VP and IBM Distinguished Engineer
Director Data Science & Engineering, Wunderman-Thompson
Software DeveloperView this speaker
CTO - Mayflower Autonomous Ship & MarineAI
Associate Professor, Statistics, UC Berkeley, co-founded Project Jupyter, NumFOCUS, BIDS, and 2i2c.org
IBM Fellow and AI Ethics Global Leader
Research Software Engineer
Chief Scientist for Geoinformatics and PAIRS Geoscope
Executive Director, LF AI & Data Foundation
Program Director - IBM Cloud and Cognitive SoftwareView this speaker
Executive Director, Institute for Computational and Mathematical Engineering at Stanford
Chief Data Science Officer, Wavemaker Global
Lisa Seacat DeLuca
Director, Emerging Solutions - Weather, Digital Twin & Agile Accelerator
IBM Machine Learning Development
AI Leader - The Weather Company
CEO, Anaconda Inc
Developer AdvocateView this speaker
General Manager of CNCF at the Linux Foundation
IBM Fellow, CTO AIOps
Chief Data Scientist IBM Center for Open Source Data and AI Technologies
Chief Scientist, IBM Research
Data Scientist / Developer Advocate- CODAIT
VP, IBM Research AI
STSM - IBM Security
STSM: Data & AI Open Tech Special ProjectsView this speaker
VP - Open Technology, CTO DEG
Principle Software Engineer, Red Hat