“We have seen ai providing conversation and comfort to the lonely; we have also seen ai engaging in racial discrimination. yet the biggest harm that ai is likely to do to individuals in the short term is job displacement, as the amount of work we can automate with ai is vastly larger than before. As leaders, it is incumbent on all of us to make sure we are building a world in which every individual has an opportunity to thrive.” Andrew Ng, Co-founder and lead of Google Brain
For decades media has romanticized artificial intelligence from as far back as the sixties with shows such as The Jetsons to modern classics such as Bicentennial Man, Blade Runner, and The Terminator.
Yet for all the anticipation each succession of the Star Wars saga creates, there remains much apprehension, excitement and wonder as to what exactly artificial intelligence or AI means from a business perspective.
The slow progress has also created confusion and misinterpretation as to what exactly is AI and how businesses can apply the innovation to spur competitive advantage.
In this episode of Podchats for FutureCIO, we spoke to Robert Merlicek, chief technology officer for Asia-Pacific and Japan with TIBCO Software to get his perspective on several issues that perplex technology, operations and business leaders as regards to what they can and cannot do with AI, as well as what they may be doing wrong with the technology in the first place.
- What is Artificial Intelligence?
- How are organisations using analytics today and how has it evolved over the years?
- What’s driving organisations in Asia to become data-driven and how does this relate to analytics and AI?
- Is AI here today and where do you see it being applied?
- How is AI being integrated an organisation’s business and operational strategy?
- How do you start an AI initiative?
- Can you cite a common pitfall with regards to the use of AI?
- How do I determine if my AI strategy is the right one for my business?
A report from Dimension Research says 80% of AI projects failed while 96% ran into problems with data quality, data labelling, and building model confidence.
Merlicek wasn’t surprised with this figure. He cautioned that as AI gets more democratised, people will try to apply Machine Learning to every problem.
“I see that the Machine Learning and AI tools are very important, but you need deep understanding of your domain area that you are applying to, as AI is there to augment your decisions. You need good quality data infrastructure, data preparation and data quality as outlined previously. Without that, how good is the AI systems you are using? The term “Garbage in – Garbage out” comes to mind,” concluded Merlicek.
He believes this just means that we still need smart humans to train the algorithms and machines how to learn.
