By: Subramanian Ananthapadmanabhan, Vice President, Enterprise Business, SAP Indian Subcontinent
One of the big topics that’s being discussed at length for the last couple of years is artificial intelligence (AI). Machine learning is emerging as the field within AI that’s seeing the most amounts of real-world applications and use cases. AI will impact almost every aspect over the next two decades and infotech is no exception.
Take, for example, a historic event that unfolded in March 2016 demonstrated the power of machine learning: the victory of the program AlphaGo over professional gamer Lee Sedol in the Google DeepMind Challenge. This exciting technological breakthrough demonstrates how far AI has come, and how it’s now able to catch humans out.
It’s true, though, that the idea of computers learning autonomously has been around for decades. So what has changed now? Why has machine learning gained so much ground in recent years and why does it continue to surprise us?
As we all know, technology predictions over multiple decades are hard. According to 1970s forecasts, mankind should have settled on the moon and Mars by now. But those who extrapolated from the first moon landings could not foresee inflection points like personal computing, the internet, smartphones and the sharing economy.
Current machine learning technology holds great potential to improve the way humans and machines work together. Machine learning can increasingly free us from many narrowly defined, repetitive, transactional tasks in a steady state. This enables us to focus on higher-value, complex tasks in dynamic environments.
In recent years, machine learning has gained ground. The technology now exists to deliver enterprise software systems that can learn how to fully automate business processes at unprecedented levels, react to real-time changes and provide the best possible results for today’s digital business.
However, even today’s most advanced machine learning algorithms learn in a very different way than humans. Without additional inflection points, extrapolating current machine learning technology to human-equivalent general intelligence would be a stretch. As we are halfway through 2017, this will continue to be a challenge for the top computer scientists.
Incorporating AI into enterprise software, for example, opens the opportunity to simplify employee’s everyday lives and allows them to focus on higher value tasks. This is necessary to move industry into the next stages of growth and innovation – at a global level.
From my own perspective, the highest potential lies in back-office functions and customer service, especially in shared service centers. As a priority, businesses and the AI community should focus on automation of repetitive tasks in transactional knowledge work. Subsequently we should tackle use cases that have not been possible before, particularly based on speech, image or video recognition.
The question for companies shouldn’t be whether to “AI or not”, but rather should be how to ensure that the world’s leading companies come together as a community to take on the heavy lifting today for the benefit of future generations tomorrow.
While it is very unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, rapid progress in the field of specialized AI will continue. It’s a fantastic time to be betting on the field and its fruit.