Steering toward tomorrow

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Talk 1 - Behavioural Cloning for Self-Driving Cars
Zara Syed - student at The Knowledge Society (TKS)

Talk 2 - Hands-On Advice for Using Quantum Computers in AI
Peter Wittek - Assistant Professor - University of Toronto

Talk 3 - Explainable AI for Enterprise Systems
Benny Cheung - Senior Technical Architect at Jonah Group

Talk 1 - Behaviour Cloning for Self-Driving Cars

Behavioural cloning is showing artificial intelligence footage of human behaviour and having it mimic what it sees. This can be applied to self-driving cars, so cars can learn to drive the same way humans learn to drive: by watching humans drive. No coder has to spend hours banging their head against the wall to manually code all the rules of driving into a car.

Bio: Zara is a 16-year-old student from The Knowledge Society (TKS), a human accelerator to build unicorn people. In her free time, you can catch her building CNNs and simulating self-driving cars.


Talk 2 - Hands-On Advice for Using Quantum Computers in AI

Machine learning and quantum computing receive much attention, and the combination of the two is a recipe for a hype. Expectations are often out of proportions, but this is in sharp contrast with the reality of quantum computing: implementations are small-scale, imperfect, they suffer from noise and poor coherence time. In this talk, we study what can be done with contemporary quantum computers. The primary algorithmic primitives are solving sampling, optimization, and some variational problems efficiently with hybrid classical-quantum protocols. The main application areas in machine learning are intrinsically discrete or probabilistic models and certain types of neural networks. We will highlight a few applications created by a new breed of startup companies in Toronto that are being incubated to exploit the relevant quantum technologies.

Peter Wittek is an Assistant Professor in the University of Toronto and an affiliate in the Vector Institute for Artificial Intelligence the Perimeter Institute for Theoretical Physics. He obtained his PhD from the National University of Singapore. His research explores the synergies between artificial intelligence, machine learning, quantum information theory, and quantum computing. As the Academic Director of the Quantum Program in the Creative Destruction Lab, he oversees two dozen quantum software startups a year that exploit contemporary quantum technologies in a commercial setting.

Talk 3 - Explainable AI for Enterprise Systems
Even with the great accuracy and precision of recent Deep Learning techniques, many enterprises are hesitant to deploy such solutions because of the lack of explainability when it comes to the results. They are responsible for many regulations that are designed to protect human rights and fairness. Without an explainable result, they have a hard time to justify their use in reality. Since the goal of Explainable AI (XAI) is an important AI's system feature envisioned by the early pioneers, we should not be blind sighted simply by the success of recent applications of Deep Learning. This presentation will discuss the rationality and techniques of explanation in a humanly understandable form.

Bio: While performing his senior technical architect roles in the Jonah Group, Benny helps to establish the Jonah’s AI Lab to expand into machine learning and deep learning business. Fully aware of the Blockchain impact on business, Benny has established a strong technical knowledge on Hyperledger and Ethereum by successfully designed and built systems for the medical health records and tokens exchange.

Benny has a B.Sc. and M. Sc in Computer Science from the University of Guelph, where his master thesis is on AI’s expert system technologies. Benny constantly posts AI, machine learning and deep learning topics in his blog site: