Day 2 - 14 November 2019
10:10AM - Day 2
AI Technology Solutions: Chair’s Welcome and Opening Remarks
CEO & Founder
10:30AM - Day 2
Transform you Customer Experience with AI and ML
This session will cover key takeaways and lessons learned from the practical deployment of ML and marketing analytics solutions to transform customer experiences across retail and supply chain verticals. We will cover a range of topics including Machine Learning, customer experiences, innovation, and business transformation.
New Markets Development
10:50AM - Day 2
Nobody wants to talk to a machine
Arthur will explore what has to happen in voice conversational AI to allow us to converse freely with machines as portrayed in sci-fi. He will share Dasha.AI’s plans to get to that future and how their product passes the Turing test and provides tangible benefits to the key stakeholders.
VP of Intelligent Enterprise Solutions
11:40AM - Day 2
11:30AM - Day 1
Driving Automation and Conversation in your Enterprise with SAP
To follow soon…
AI Platform Leader
Sirius Computer Solutions
12:10PM - Day 2
Effective Enterprise Grade Machine Learning that Drives Measurable Value
12:30PM - Day 2
03:30PM - Day 1
Automating AI/ML Data Prep
- Why 100% automation of data prep
- Creating Knowledge Graphs
- Enabling Contextual Intelligence
Chihuahua or Muffin? Object recognition
- Challenges of developing object recognition systems
- Training models using large image data sets
- Training deep neural networks
- Testing with real-life images
Senior Project Manager
02:10PM - Day 2
Applications of AI in the Legal Industry
- Introduction: Using AI to process large amounts of legal documents
- Setting up a model: defining terms
- Building a searchable terms database and using it to automate workflows
- Benefits of automation
Machine Learning Lead Architect
02:30PM - Day 2
Automatic Machine Learning & Symbolic Neural Networks in the Wolfram Language
As part of the Wolfram Language (a.k.a. Mathematica), we developed efficient yet user-friendly machine learning tools aimed to be used by both beginners and experts in the field. These tools include a symbolic neural network framework, a repository of pre-trained networks, and fully automated machine learning functions. I will give an overview of these tools, live-demonstrate their capabilities, and talk about the technical solutions used under the hood. I will then present some applications we developed using these tools, such as a named-entity recognizer able to identify hundreds of entity type.
Zipline: A declarative feature engineering framework
In this talk we will introduce zipline, a feature engineering framework developed at airbnb to help data scientists take features to production – safely and quickly.
Zipline is used to
a.) Generate point-in-time correct feature backfills for model training with high efficiency,
b.) Generate online feature serving pipelines that can serve realtime feature aggregates with high availability, while also guaranteeing online-offline consistency.
c.) Incorporate Change Data Streams into features for sub-second data freshness
NGD Systems, Inc.
03:30PM - Day 2
Realizing AI Potential in Mass Datasets with Computational Storage
As we generated massive amounts at the edge and beyond and are relying on AI to analyze this data. We are stuck dealing with getting this data moved between storage and compute. With the advent of Computational Storage we are able to overcome the gravity of the data and allow AI to derive value from the data stored vs just waiting for the data to be moved from place to place.
In this talk, I will showcase how AI running at the Storage Device level in a Computational Storage Drive (CSD) is unmatched in value being derived from the data stored.