<img height="1" width="1" style="display:none;" alt="" src="https://dc.ads.linkedin.com/collect/?pid=306561&amp;fmt=gif">

Artificial Intelligence vs Automation

Written by Dr. Mark Nasila

Artificial Intelligence vs Automation

Written by Dr Mark Nasila on May 7, 2019 1:14:10 PM

CDAO Africa CDAO Africa Insights

AI is often confused with automation, yet the two are fundamentally different. Dr Mark Nasila, FNB’s Chief Analytics Officer for Consumer Banking, explains the key difference is that AI mimics human intelligence decisions and actions, while automation focuses on streamlining repetitive, instructive tasks.

Automation has been around for some time and is probably so integrated into most business operations that it’s not obvious – for example, the auto-generation of marketing emails and SMSs to customers and even customer statements for specific periods,’ he adds.

 

Automation saves time and money spent on monotonous, voluminous tasks and gives employees an opportunity to apply themselves to more complex processes.

 

Artificial intelligence deals with technologies, systems or even processes that competently mimic how human beings make decisions, react to new information, speak, hear, as well as understand language.’ Mark says it helps to understand machine learning as a subset of artificial intelligence. ‘Machine learning enables systems and processes to learn from data, identify patterns and recommend decisions without human involvement.

 

Deep learning is defined as a subset of machine learning where artificial neural networks – algorithms built around the neural structure of the human brain – learn from data. The same way human beings learn from day-to-day events over time, a deep learning algorithm executes functions repeatedly and continuously learns and adjusts itself to improve accuracy. We call them deep learning algorithms because the neural networks have various (deep) layers that enable learning of complex patterns in large amounts of data.’

 

Mark uses Facebook’s facial recognition application DeepFace as an example. ‘Facebook uses deep learning to analyse every photo I have ever been tagged in to arrive at a set of features of my face, called a template. The algorithm does the same for millions of other Facebook users based on their unique set of features. Let’s say I post a picture of myself on Facebook with a group of people, it will recommend I tag myself when the model is confident that it is me based on a probability score.’ Facebook says DeepFace has a 97% success rate in recognising whether two images are of the same person or not – compared to 96% for humans.

 

Is AI replacing human jobs?

On the contrary, according to a report by global IT consulting firm Gartner, AI is estimated to create around 2.3 million opportunities by the year 2020. ‘You’ll find a pool of talented people behind every project,’ says Mark. ‘Each use case requires a machine learning team to drive it. Uber, for example, created a whole range of jobs to teach machines how to understand customer demand, traffic and safety.’ He says it’s no different in our business. ‘There is huge potential for jobs in the future – all it takes is a willingness to adapt to work alongside machines.’

Related posts