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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 Agenda: 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.
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) * 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 - 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. * 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. * 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. * 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 - 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.
The date and locations are to be updated. Speaker Sheldon Fernandez received UW’s Young alumni achievement medal at Waterloo for his professional and humanitarian endeavours, has substantial experience in this area, having spent time on the ground in Kenya after the country’s tumultuous election in 2007. He has also studied the topic extensively, most recently at the Montreal Institute for Genocide and Human Rights Studies at Concordia University. Sheldon is currently the CEO of DarwinAI. * More about the presentation The explosive progress of Artificial Intelligence and Deep Learning systems has given rise to a new and flourishing area of research termed Computational Creativity. A multidisciplinary enterprise that resides at the intersection of AI, philosophy, cognitive psychology and the arts, this evolving discipline continues to challenge our assumptions about the limits of non-human forms of intelligence and our relationship with machines. We have reached the stage where software can generate pictures, melodies, jokes and poems, can invent new words and even discover new and interesting mathematical theorems. In addition to probing the scientific underpinnings of creativity, such endeavours raise fascinating questions about the nature of intentionality, originality, and inspiration. In this talk, we will explore such topics as: - What is the relationship between creativity and computation? - Can the latter truly beget the former and if so, how? - How do we evaluate whether something is truly creative given the emotional, subjective, and often contradictory qualities of creativity itself? - And, most importantly, if creativity is partially about ‘discordance’ – setting one’s mind free from the worn channels of tradition to concoct something novel – how can an algorithmic system ever be creative? Phrased alternatively, if a machine can do only what it is programmed to do, how can it ever exhibit what might be termed creative behaviour
Speaker 1 - Philippe Beaudoin (SVP Research Group and Co-Founder at Element AI) Speaker 2 - Nima Ashtari (CEO - X-Matik Inc.) Speaker 3 - Frank Rudzicz (Scientist at Toronto Rehabilitation Institute) * Talk 1 - Details soon Talk 2 - Details soon Talk 3 - Details soon