AI Australia
AI Australia
Feb 18, 2019
Societal Automation Challenges, Ethics, Benefits, and Requirements with Chris Hausler
Play • 54 min

Today we’re speaking with Chris Hausler, Senior Data Science Manager at Zendesk, a global customer support SaaS company.

Chris believes that the future of AI and humanity will be a bright one, but humans must adapt and seek new opportunities as old opportunities evaporate. Hausler is working to help clients resolve customer service issues before they crop up by giving users access to the information they need at their fingertips.

Hausler speaks about artificial intelligence growth, development, and trends over the last few years and how Zendesk has helped set the bar for their development on today’s episode of AI Australia

  • 02:00 How did Zendesk integrate AI into their already strong ticketing system? How is it changing customer-facing features?
  • 02:40 How are Chris and his teams able to find repetitive trends in support conversations,  streamline the information, and add UX elements for self-service.
  • 05:40 What was the first machine learning project Chris worked on with Zendesk?
  • 09:20 Why does Zendesk keep all data science in Melbourne as opposed to near their home office in San Francisco?
  • 14:30 Do you find that talent is more readily available in Melbourne? Is it less competitive than San Francisco? What challenges do you face while recruiting in Melbourne?
  • 20:10 What skills and traits do you look for when hiring smart people straight out of university?
  • 22:50 When approaching problems, what critical thinking skills come into play? How can these be applied to a machine learning platform?
  • 28:00 What technologies have been responsible for machine learning growth over the last four years?
  • 33:10 Will humans take action to “hack” self-driving cars? What sorts of action might they take?
  • 38:10 Where do you stand on dystopian versus optimistic points of view?
  • 40:10 What are some upcoming breakthroughs that are coming up on seriously changing the world?
  • 47:00 Can machine learning be used to teach products to change their approach based on the customer experience?
  • 48:00 What other outcomes, aside from job loss are possible through machine learning applications in healthcare?
  • 49:40 What roles on a team are needed to start hiring for a machine learning business path?
  • 50:10 What skills are sought after for early hires in data scientist?
  • 50:50 How important is domain knowledge in data science?
  • 55:00 Are encrypted backdoors a serious threat to information security? Should legislators be allowed to make these mandatory?


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