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Top Ten Takeouts: Implementing an AI Solution

It’s a subject that has sparked thousands of sci-fi novels and movies. It has divided academics like Noam Chomsky and Paul Saffo, and brought Tech Superstars Elon Musk and Mark Zuckerberg to (Twitter) blows. The Churchill Club’s panel, ‘Implementing an AI solution - what you need to know’ was a much needed reality check on the practicalities, applications, benefits, and limitations of AI solutions at present and in the near future.

The spectrum of AI subsets and applications is influencing processes and interactions across industries - from banking to healthcare. As the technology evolves, businesses are scrambling to catch up in skills, understanding and approach.

We explored 

  • What is AI? And why can that be a tricky question to answer?
  • Where can we see AI being used now and what results is it yielding?
  • Where is AI headed in the future?
  • Should you build your own AI solutions or buy ‘out of the box’?
  • Is Australia ready and skilled-up for the AI revolution?

With panelists

Jonathan Chang - Managing Director, Silverpond
Karin Verspoor - Professor, School of Computing and Information Systems, University of Melbourne
Soon-Ee Cheah - Data Scientist, Zendesk
Mark Moloney - General Manager, Big Data Analytics, Telstra

The Takeouts

  1. An ‘altruistic algorithm’ is the ultimate objective, where planning and purpose meet to understand and achieve goals.  
  2. Deep Learning is inspired by the brain, but at a superficial level and while algorithms are becoming increasingly sophisticated it is debatable as to whether they truly understand, or simply mimic.
  3. While solutions such as Watson have made AI more accessible, a data specialist can apply specificity to ensure the solution is tailored to your market.
  4. Not all data is created equal and boxed solutions are created with a specific customer and industry in mind. A data scientist can direct the use and labelling of data, and appropriate tools based on the business’ need.
  5. Without the added value of a business’ own data and domain knowledge, you can undermine the solution and miss the opportunity to leverage your unique insights.
  6. Bias has been a well-publicised flaw of AI and demonstrate the influence humans have over AI, as the unconscious bias of data scientists present themselves in the solutions they develop
  7. Manage expectations - it will take longer than you imagine, particularly for data collection and labelling
  8. Ensure you have a good team - tech is one piece of the puzzle, but you need a good team to execute it
  9. AI does not occur in a vacuum - investors and key business stakeholders need to understand and trust the process
  10. Packaging and testing are crucial to confirm that the solution operates effectively within the context it was intended for. 

Thanks to our venue partner, Zendesk for hosting the evening.