Filtering by: Business Track

Predicting Power of Emotions: Australian Election Case Study — Lisa Lewers & Lana Novikova (Heartbeat AI Technologies)
Oct
11
2:30 PM14:30

Predicting Power of Emotions: Australian Election Case Study — Lisa Lewers & Lana Novikova (Heartbeat AI Technologies)

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Finding value in legacy industrial datasets: lessons from Amey’s Mercury platform — Stephen Gooberman-Hill (Amey)
Oct
11
2:00 PM14:00

Finding value in legacy industrial datasets: lessons from Amey’s Mercury platform — Stephen Gooberman-Hill (Amey)

Amey is one of the UK’s leading engineering asset management companies. We manage the design, build and maintenance of large public infrastructure estates – our clients include rail operators, airports and public utilities. The assets are specified for long lifetimes – an escalator is designed to last 30 years; a bridge hundreds.

Many of these assets are instrumented via a variety of legacy systems. We have designed and deployed a system called Mercury, which builds models of asset performance from this instrumentation data, and combines it with work order and other maintenance data to allow operations and maintenance teams to understand the performance of their assets.

Machine learning is integral to Mercury, problems include free text matching, anomaly detection and fault prediction. I will talk about our experiences of applying ML techniques into legacy asset datasets, the issues we have faced, and how we have been able to provide actionable predictions of upcoming asset failures.

Dr Stephen Gooberman-Hill is a Principal Consultant in Amey’s Strategic Consulting and Technology Group. He is the solution originator of Amey’s Mercury data analytics system. He is currently managing a number of Mercury pilot deployments, and is also developing innovative data gathering and analytic solutions across a range of customers and partners.

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The Smarts in Smart Home Health — Sean Lorenz (Senter)
Oct
11
1:30 PM13:30

The Smarts in Smart Home Health — Sean Lorenz (Senter)

Digital health has a problem. There are plenty of mobile applications being built to tackle just about every health-related issue out there, but most of these apps still lack quantitative data, predictive intelligence, and clinical validation. In this talk, Sean will discuss how we can aggregate smart home, wearable, connected health and ingestible data to create smarter, adaptive applications that patients, caregivers and providers can use to stay healthy outside the walls of a hospital.

Sean Lorenz is Founder & CEO of Senter, a startup creating a smart home health hub for healthy aging, as well as CTO for the Aging Well Institute. Dr. Lorenz was recently the Director of IoT Market Strategy for LogMeIn’s IoT platform, Xively. He has shaped business models and product strategies in several emerging markets including IoT, robotics, artificial intelligence and healthcare. He holds a PhD in Cognitive & Neural Systems from Boston University.

Twitter: @seanlorrenz - Linkedin

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BayesDB and VizGPM.js: open-source AI for visually exploring complex databases — Richard Tibbetts (MIT)
Oct
11
12:00 PM12:00

BayesDB and VizGPM.js: open-source AI for visually exploring complex databases — Richard Tibbetts (MIT)

Artificially intelligent data products don’t have to be limited to answering simple natural language queries. Navigation, search, and retrieval of structured data, even by sophisticated domain experts, benefit from using AI to infer data’s latent structure. Using open source BayesDB and VizGPM.js, we demonstrate interfaces for browsing US census and software performance data.

Richard Tibbetts is a software entrepreneur, database and programming languages nerd, a Visiting Scientist at MIT Probabilistic Computing and a leader of the "BayesDB": probcomp.csail.mit.edu/bayesdb open source project. Prior to MIT Richard was founder and CTO at StreamBase, a CEP company that merged with TIBCO in 2013. Richard is also the CEO of Empirical Systems a stealth mode startup.

Twitter: @tibbetts - Linkedin - Website

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Prediction | Production: Lessons from ‘Over-the-wall’ — Stuart Bailey (Open Data Group)
Oct
11
11:00 AM11:00

Prediction | Production: Lessons from ‘Over-the-wall’ — Stuart Bailey (Open Data Group)

Data Science and Dev Ops teams live on opposite sides of a wall in most organizations. Despite the separation, these teams should work together to develop a coherent process to release analytic products, support those products and maintain sanity. We propose an institutional capability, ‘Analytic Operations’, to support data-driven processes within lines-of-business. We hope to share lessons learned practicing Analytic Ops and present a set of best practices for Analytic Ops teams. We also demo open source tools that reduce frictions between Data Science and Ops/Deployment teams.

Stuart Bailey is a partner and the Chief Technology Officer at the Open Data Group. He is a technologist and entrepreneur who has been focused on analytic and data intensive distributed systems for over two decades. Prior to Open Data Group, Stuart was the founder and most recently Chief Scientist of Infoblox (NYSE:BLOX), a Sequoia Capital-backed company. More than half the Fortune 500 rely on the Infoblox automated distributed system solutions for essential, software-based network control.

Twitter: @stu_bailey - Linkedin

 

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