"From predictions to prosperity" and "The Talking Machine"

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Meeting sponsored by IP Factory | Omnia AI | Deloitte’s in-house AI enabled software development team

We invite you to join us for an exclusive event at Deloitte Bay and Adelaide downtown office.

We are looking for like-minded people to come to discuss the future of AI in Canada and possibly join us to build it together.

RSVP: Email Arushi Chauhan archauhan [at] deloitte . ca

5:30 – Registration
6:00 – Ofer Shai – Canada’s AI imperative: From predictions to prosperity
6:30 – Omar Khalil – The Talking Machine - Sequence to Sequence Learning Using Deep Neural Networks
7:00 – Networking

We will also have someone from HR joining us, as we are looking for senior and up level talent to exponentially grow the team.

Talk 1 - Canada’s AI imperative: From predictions to prosperity

Artificial intelligence (AI) is expected to be one of the leading economic drivers of our time, and Canada has the opportunity to be a global leader. We have the research strength, talent pool, and startups to capitalize, but that’s not enough if we truly want to lead in an AI-driven world and shape what it might look like. True leadership is required—that means taking steps now to establish a world-class AI ecosystem in Canada.

Bio: Ofer Shai
Ofer is Director in Omnia AI, Deloitte’s AI practice, where he is leading the IP Factory. Ofer uses the latest in deep learning, machine learning and AI to build products and solutions for Deloitte’s clients, and drive AI strategy and insights in the market. Omnia AI’s IP offerings include solutions designed to help enterprises manage their contracts and re-papering load, media and social media monitoring to detect and predict strategic risk, AI-driven cybersecurity tooling, health care solutions for improving caregiver and patient experience, and more.

Ofer has 15 years of experience in natural language processing, predictive and advanced analytics, recommendation systems, information retrieval, and computational biology. Ofer holds a Ph.D. from the University of Toronto Machine Learning Group and has held leadership positions at Upsight, Meta, and Chan-Zuckerberg Initiative.


Talk 2 - The Talking Machine - Sequence to Sequence Learning Using Deep Neural Networks

These are tasks where a machine receives a sequence, most popularly text, as input such as “My name is Omar” and learns a mapping to the desired output which is also a sequence, such as “Je m’appelle Omar”. Many interesting applications fall under this general framework, aside from translation we have Q&A pairs where the question is the input and an answer is the desired output, predicting the next sentence in a text, and, counter-intuitively, networks that learn to return the same input back, and in the process of deconstructing and reconstructing the input learn useful structure about the sentence. If you’re playing Jeopardy you can even start with the answer and the desired output is the question. The talk will delve into the relatively short but rich history of these models that started in 2014, including delving into the technical details behind the different model architectures, from LSTM based encoder-decoder models to the introduction of attention mechanisms, to relying solely on attention, and finally bidirectional attention-based models.

Bio: Omar Khalil
Omar is a Lead in Omnia AI, Deloitte’s AI practice, where he leads the natural language processing team for dTrax, Omnia’s legal contract analysis tool. He has been with Deloitte Canada for three years, and his prime competency is in machine learning, in particular, the use of deep learning techniques in NLP.