Schedule

Throughout these two days you’ll be able to hear over 20 top-of-the-line speakers from the AI industry. Both days will be filled with some amazing talks with topics ranging from deepfakes and use of AI in the justice system to ways of making machine learning more efficient. Have a look at each talk and its description in our schedule.

Monday
6/4/2020
Tuesday
7/4/2020
10:00
10:00 - 11:00
Registration
Main Stage
Pick up your badge, familiarise yourself with the schedule and the venue's layout and meet other attendees before things get hectic.
11:00
11:00 - 11:15
Opening Ceremony
An opportunity to see all of the people in one place, before everybody heads to workshops, and lectures
11:15
11:15 - 11:45
Evolution of Data Science & ML/AI in Tech:
from Insights to Experiment-Backed Data Products
Lin Huang (Reddit)
Success of data science requires mastery of ML/AI techniques, and deep understanding of company priorities. The most rewarding yet challenging aspect is to build a rhythm to deliver actionable outputs (i.e., data products) that directly transform business decisions. In tech companies, we are blessed with uniquely complex data assets, for which it took us rounds of iterations to customize a solution that maximizes our data potential. This talk focuses on my journey as a leader and practitioner in the tech industry in the last decade, especially the last 18 months at Reddit to build an experimentation platform, and to revamp the engineering workflow prioritizing the interplay of offline ML modeling and online testing. Not every piece of ML insights checks out in an experiment, but every "setback" is equally valuable as it allows us to iterate faster and more purposefully. Culturally speaking, I encourage calculated risk taking since data products are most powerful when they point to new business directions (vs. validating the existing ones). With a suite of concrete examples, I demonstrate how rhythmically outputting data products answers the common questions that almost all serious tech companies are curious about, namely, where are “good ideas” from? How to generate them at scale? Do they hold up IRL? Still valuable if they don’t?
11:45
11:45 - 12:15
Graph Deep Learning for Real-World Applications
Mark Weber (MIT-IBM Watson AI Lab)
Try to reason about something without any context. It’s possible, but your understanding will be limited and brittle. That’s because relationships between things give us critical information. In mathematics, we can model relational data as a graph or network structure -- nodes, edges, and the attributes associated with each. While deep learning has done remarkable things on Euclidean data (e.g. audio, images, video) graph deep learning has lagged because combinatorial complexity and nonlinearity issues making training very difficult and expensive. Yet it’s precisely the information hidden in that complexity that makes graphs so interesting. In this talk, Mark Weber will introduce a class of methods known as scalable graph convolutional networks (GCN) and share experimental results from a semi-supervised anomaly detection task in financial forensics and anti-money laundering. We will take a closer look at a new method developed at MIT-IBM called EvolveGCN, which uses recurrent neural network architectures (RNN) for handling temporal dynamism. We will discuss the implication of these results in anti-money laundering and beyond.
12:25
12:25 - 12:45
TBA
TBA
12:45
12:45 - 13:00
TBA
TBA
13:00
13:00 - 14:00
Lunch Break
Everybody needs fuel, right?
14:00
14:00 - 14:45
Shift Challenge - Semi Finals 1
TBA
14:45
14:45 - 15:30
Recommendation Systems at Scale
Ashish Bansal (Twitter)
15:30
15:30 - 16:00
TBA
TBA
16:00
16:00 - 16:20
Coffee Break
Everybody needs fuel (part two), and of course a great networking opportunity
16:20
16:20 - 16:45
TBA
TBA
16:45
16:45 - 17:30
Shift Challenge - Semi Finals 2
TBA
17:30
17:30 - 18:00
Mission Collision: Artificial intelligence in the era of social justice reforms
Kairan Quazi (Intel)
A daily scan of news headlines shows that technology has become invasive and pervasive in every aspect of modern civil life. The increasing use of artificial intelligence technology by law enforcement and criminal justice institutions is raising serious issues for civil liberties and social justice. “Bad data” disproportionately targets already marginalized populations that have faced historical systemic discrimination. This discussion will touch on the consequences of the current application of Artificial Intelligence technology on initiatives to achieve equity and justice in our civil institutions.
18:00
18:00 - 18:30
Operationalizing Machine Learning in Enterprises: How companies can manage the entire data science process and productively use machine learning with ease
Sebastian Wieczorek (SAP)
Machine Learning made large progress in recent time, and it has an enormous potential to revolutionize and simplify the daily work in companies. However, according to Gardener, this potential is still not yet fully exploited, and the biggest issue for companies nowadays is to deploy machine learning in production and make really use of it for their productive processes. In this Session we show how SAP manages development, training, deployment, and operation of Enterprise AI applications for building the intelligent Enterprise.
18:30
18:30 - 18:45
TBA
TBA
21:00
21:00 – 02:00
After Party
Chillax, network and mingle. You won't be sorry
10:00
10:00 - 11:00
Registration
Pick up your badge, familiarise yourself with the schedule and the venue's layout and meet other attendees before things get hectic.
11:00
11:00 - 11:30
TBA
TBA
11:30
11:30 - 12:00
Ethically High Risk AI Applications
Kathy Baxter (Salesforce)
AI has the potential for significant good in the world; however, there specific AI applications that are at high risk of directly or indirectly violating human rights. It is important to recognize why these applications are high risk, determine if the risks can be mitigated, and when or if they should be used at all.
12:00
12:00 - 12:15
Doing AI Responsibly: Tools for Practitioners and Regulators
Ashley Casovan (AI Global)
With AI poised to add more than $15 trillion dollars to the global economy with limitless opportunities to provide better access to critical services like healthcare, banking, and communication, AI has proven the ability to enhance our daily lives and increase the analysis of information. However, early uses of AI have demonstrated that when not designed in a thoughtful and responsible manner, these systems can be biased, insecure, and not compliant with existing laws. The issues arising from the increased use of AI have been well documented including, hundreds of white papers, policies, journal articles, and research studies. However, there are limited resources available for practitioners to easily understand what developing good AI actually means to them. The presentation will give an overview of the responsible AI landscape as well some of the tools being developed to support those building AI systems from the engineers to the product managers to the lawyers.
12:15
12:15 - 13:00
TBA
Julia Choi (Intel)
13:00
13:00 - 14:00
Lunch
Everybody needs fuel, right?
14:00
14:00 - 14:45
Shift Challenge - Finals
TBA
14:45
14:45 - 15:30
TBA
TBA
15:30
15:30 - 16:00
TBA
TBA
16:00
16:00 - 16:20
Coffee Break
Everybody needs fuel (part two), and of course a great networking opportunity
16:20
16:20 - 16:45
AI on the Edge
Michael Lanzetta (Microsoft)
Tales from the field – challenges and opportunities deploying AI models in various Edge and IoT scenarios. My team works with everyone from our largest customers to some of the world’s largest mammals (anti-rhino-poaching efforts), building machine and deep learning models and deploying them into production everywhere from the cloud to birds’ nests.
16:45
16:45 - 17:10
TBA
TBA
17:10
17:10 - 17:35
TBA
TBA
17:35
17:35 - 18:00
Shift Challenge Winner Announcement
A look back onto these two days, a few closing words and a warm goodbye until the next year.
18:00
18:00 - 18:15
Closing ceremony
21:00
21:00 – 04:00
After Party
Last day, closing party. Go all out…