Automation could solve the machine-learning talent shortage

Ahead of this year's Big Data & Innovation Summit in Beijing, we sat down with Darko Matovski, CEO of CausaLens, to discuss the future of machine learning, automation and smart cities


If you have spoken with any data scientists, CDOs or any other data-focused professionals in the last five years,

you would likely have heard observations on the lack of talent entering the field – an issue clearly visible within the AI and machine-learning (ML) sectors.

CasuaLens is attempting to bridge the talent gap through its platform that specializes in automating data exploration, model building and deployment. We spoke with its CEO, Darko Matovski, to discuss some of the challenges the company faces and where he thinks machine learning and automation may be taking us.

Innovation Enterprise: Can any city become "smart" including ones with historical infrastructure such as London or Shanghai?

Darko Matovski: Definitely. Sensors are becoming smaller and cheaper, while 5G is going to facilitate their connectivity.

IE: What are "time-series predictions" and what do they mean to companies?

DM: Dynamic systems generate time-series data. The world's economy is one of the most complex dynamic systems. Measurements about economic activity are represented in a form of a time-series. For example, by being able to accurately predict demand for its services, an organization could optimize resource allocation.

IE: What is one of the most exciting utilizations of IoT?

DM: We are very excited about optimizing the global economy with predictions based on data from IoT devices. For example, we could reduce at least 10% electricity usage if companies could accurately predict demand for electricity. Food losses and waste amounts to $1 trillion. Half could be saved if we could predict microclimate and demand for food in real time.

IE: How vital is machine learning automation to the future of industry? Is being utilized to its full capacity yet?

DM: There is a huge shortage of machine learning talent globally. Automation is the only way. And no, I think we are just beginning to scratch the surface.

IE: How can we expect time-series predicting to change our lives in the near future?

DM: Organizations utilizing predictive technology are more efficient and can offer better quality services at a better price. We all benefit if we can predict dynamic systems in real time.

IE: Finally, how do you see IoT taking larger prominence in people's everyday lives?

DM: I think the cost of sensors and 5G are the key drivers. Naturally, IoT devices will proliferate in our everyday lives and in the workflows of organizations.

To find out more on how you can learn from the world's top leaders in A.I. and big data, see Darko Matovski speak at Innovation Enterprise's AI & Big Data Innovation Summit in Beijing on November 21–22, 2018.

Book your place HERE.

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