Discover how to ensure your company is developing and deploying AI in a responsible, ethical and fair way to benefit all.

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Maithili shares her thoughts in this article on what is key to Canada's competitiveness in the global ideas economy

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“Today’s models can be trained on huge quantities of data, but that’s not enough,” says Bengio, who together with LeCun and Google’s Geoffrey Hinton is one of the original musketeers of deep learning. “We need to discover learning algorithms that can take better advantage of all this unlabeled data that’s sitting out there.”

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"To understand deep learning in the context of genetic disease, you need to understand shallow learning first. Shallow learning relates mutations to diseases by looking for mutations that commonly occur in patients with a disease. It’s a commonly used method", Brendan Frey, Deep Genomics.

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Conservation Metrics, a recipient of Microsoft’s AI for Earth grant program, is using algorithms to analyze a corpus from Cornell University Lab of Ornithology’s Elephant Listening Project, which collects data from acoustic sensors embedded throughout Nouabalé-Ndoki National Park and adjacent logging areas in the Republic of Congo.

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Calico was created by Google back in 2013 to tackle human aging and diseases associated with age. "“At Calico, I will work on the development of new computational methods for analyzing biological data sets, to help move to achieving these important scientific and societal goals,” said Koller."

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IBM Watson A.I. Xprize: A Cognitive Computing Competition: will invite teams from around the globe to come up with their own challenges that demonstrate A.I.’s potential to positively impact people’s lives.

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Timnit Gebru, Research Scientist in the Ethical AI Team at Google, discusses the effects of bias in artificial intelligence. Timnit shares with Bloomberg that "we can try to mitigate bias and we can try to mitigate the effects of bias".

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Why Attend

Speakers

Questions explored

  • How do you create a responsible AI strategy?
  • What governance structures are required?
  • Who should be responsible for ethical AI?
  • How to ensure a human-centric approach to AI
  • How can you embed trust & transparency?
  • What are real examples of responsible AI in business?
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A cross-disciplinary mix of pioneers will share expertise, vision and leadership to provide actionable advice and strategies for enterprises, government, startups, academia, industry & NGOs to develop and progress AI in a responsible, accountable and transparent way.

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Topics covered

  • Diversity
  • Bias
  • Privacy & Security
  • Accountability & Transparency
  • Responsible Enterprise AI
  • AI for Good
  • Ethics
Who

Who Should Attend

  • CEOs, CIOs, CAIOs
  • Chief Data Officers
  • Founders
  • AI Strategists
  • Policy & Gov Advisors
  • Professors
  • Ethics Leaders
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Join the discussion

  • 75 speakers
  • 700+ leading technologists & innovators
  • Group brainstorming sessions
  • Interactive workshops
  • 7+ hours of networking
  • Access to all the filmed presentations
  • Montréal Declaration for a Responsible Development of AI
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Topics we cover

Ethics
Ethics
Business Case Studies
Business Case Studies
Increase collaboration
Increase collaboration
AI Implementation
AI Implementation
Security
Security
Bias
Bias
Transparency
Transparency
Accountability
Accountability

Regular Attendees include:

Google
UC Berkeley
Accenture
The Knowledge Academy
IBM
Microsoft
openai
GovTech
Universite de Montreal
CIFAR
Montreal AI Ethics Institute,
Global Public Affairs Canada

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