Name: How about the future?
Start: 2019-02-11T18:00:00-05:00
End: 2019-02-11T20:30:00-05:00
Location: RBC Centre

Talk 1 - Self Driving Cars (SDC) - past, present, future - Marius Slavescu (Founder at Open Source Self Driving Car Initiative)

Talk 2 - Linking records across datasets: challenges and solutions - Mehrsa Golestaneh

Talk 3 - Using the fundamentals of data science in the development of an economic roadmap which is currently being disrupted by AI - Richard Boire (Senior Vice President - Environics Analytics)

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Talk 1 - SDC - past, present, future - Marius Slavescu

An objective view of current SDC technologies, hardware and software, touching on open source projects in this area.

Present a few ideas on how this technology will transform our daily life, from kids to seniors.

What opportunities will arise in the autonomous space, from career and DIY automation perspective, and how to prepare for them.

A few short (live) demos of computer vision and deep learning, applicable from toy to full-sized SDCs.

Links:
https://github.com/OSSDC/OSSDC-Hacking-Book/wiki/Authors-and-editors

https://github.com/OSSDC/OSSDC-Hacking-Book/wiki/Vision-and-Mission

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Bio: Marius Slavescu is the founder of the Open Source Self Driving Car and GTA Robotics communities and co-author of STEMCA Inventor, a robotics/STEM education and DIY/maker platform.

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Talk 2 - Linking records across datasets: challenges and solutions - Mehrsa Golestaneh

Have you gathered financial information from various sources and need to aggregate them to see the complete picture? Are you looking into bringing patients information from various sources to one place and merge them all to extract better insights? Is linking internal company data to valuable external datasets crucial for what you want to achieve?

In all these scenarios, most of the time there is no robust indicator, or common key, to tell us which rows are talking about the same entity (ex. company, address, product, etc.), and we need to engineer efficient solutions, using AI and machine learning, to link these datasets to each other.

In this talk, we go through some of the main challenges, and propose smart solutions for tackling them.

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Bio: Mehrsa Golestaneh is the Principal Data Scientist at ThinkData Works Inc. She leads the research and development efforts in developing scalable machine learning solutions for data management, data enrichment, and predictive analytics.

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Talk 3 - Using the fundamentals of data science in the development of an economic roadmap which is currently being disrupted by AI - Richard Boire

As data scientists, we are very cognizant of the disruptiveness which is and will be caused by AI. Its impact on the economy and jobs has been touted as having its largest influence. A study by McKinsey revealed a 30% elimination in human labour by 2030. (https://www.iotforall.com/impact-of-artificial-intelligence-job-losses/)

A very scary prospect. As practitioners who work with data and various machine learning tools, why don’t we use our skills to put forward a white paper that outlines suggested transformational changes within our society? It is our group, who is most knowledgeable in this area of automation and AI, that should be one of the key change agents in this transformational process. Let’s start the dialogue

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Bio: Richard Boire's experience in data science dates back to 1983 when he received an MBA from Concordia University in Finance and Statistics complemented with career experience at leading-edge organizations such as Reader’s Digest and American Express.

Through his work at Boire Filler Group and most recently at Environics Analytics, Richard has become a recognized authority on predictive analytics in Canada with unparalleled practical experience and expertise across virtually all business sectors. Richard published a book in 2014 entitled “Data Mining for Managers: How to use data (big and small) to solve business problems” which was published by Palgrave McMillian of New York City.

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Marius Slavescu

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