In today’s IT world, everything is about being fast, flexible and efficient. We work agile, build prototypes and use fast-scaling adoptive cloud infrastructure. At the same time, the advances in the fields of AI and machine learning seem to make a business world possible, in which many tasks are optimized or even taken over by learning algorithms and intelligent software.
But whoever thinks of this world in terms of just picking out the low-hanging fruits from the growing AI-tree might be in for a big disappointment. Sure, rapid hardware advancements and cloud infrastructure enable fast computations but they don’t solve one of the core challenges inherent in the way most algorithms learn: based on mathematical properties that are deducted from input data.
Of course, this is not a secret at all. In many discussion and contributions focusing on the limits of AI and machine learning this topic comes up quickly. As Peter Guy puts it in the South China Morning Post: AI is only as smart as the given data input or Jason Pontin on WIRED: “new situations baffle artificial intelligences, like cows brought up short at a cattle grid”. However, this limitation does not seem to be very prominent when businesses envision the great potential they see in AI bringing them forward. Gartner estimates that in 2021, AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity (see “Forecast: The Business Value of Artificial Intelligence, Worldwide, 2017-2025”).
How much time and effort are we willing to spend training machines?
In order to meet these high expectations regarding AI to leverage business processes and boost productivity, we need to deal with the dependency on input data when learning. Otherwise, I’m seriously wondering who is going to teach all those machines. Or else: who is going to get them all the adequate data input they need in order to drive forward a business? When you listen to Michael Chui and James Manyika in their McKinsey podcast about the real- world potential and limitations of artificial intelligence, you get an impression of how tedious and time consuming it can be to teach and generate adequate training data for only one specific machine learning task. And how much this is often underestimated when thinking about “self-learning” machines.
This underestimation could grow into a serious issue because in order to learn properly, algorithms have some requirements, which seem to become scarcer in today’s IT and business: time, consistency and extensive variety. If we want algorithms to pick up patterns and machines to make smart decisions, we need to teach them over time. Or at least give them data, rules and an environment in which they can…Continue reading
Article Source: https://dataconomy.com/2018/10/how-learning-processes-in-artificial-intelligence-ai-can-improve-business-decisions/
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