Why a leading economist is embracing machine learning

Have you heard about the new ways to reduce costs and increase profit? Machine learning in economics highlights these patterns. According to recent research, this can increase productivity by 14.3% by 2030. It is a stimulant for productivity growth. Shortly, many current jobs and tasks will be performed totally by Artificial Intelligence (AI) algorithms or their usage. It will keep snowballing, and the impact on the market will soon become fundamental.  

AI in economics 

These two topics – Machine learning and artificial intelligence (AI) – are burning issues today. 

They may add to economic growth in three main areas:

  • Improvement of productivity,
  • Product enhancement,
  • Stimulating new companies.

These three areas are essential for economics and market development. This new way will have a massive kick on the market’s and society’s growth. 

Machine learning will be a necessity for every new company entering the market. 

Plus, AI warrants excellent benefits for productivity and the economy, but it could also displace existing jobs simultaneously as it creates new ones!

Entrepreneurs aim today for autonomous AI that won’t need human intervention or work to make highly complicated decisions. 

That means they want to use it everywhere, whether financial services, healthcare, energy and mining, industrial products, or media and entertainment.

Companies may start with low-risk and high-return pilot programs. However, for long-term benefits, they may need to:

  • Align AI strategy with business strategy,
  • Develop enterprise-wide AI capability,
  • Build a standardized portfolio of AI capabilities,
  • Establish AI-appropriate governance for security and risk mitigation.

What can machine learning add to economics?

We know that machine learning, along with economics, is based on data. So, we have now two approaches: traditional, which is econometrics, and innovative, which is machine learning. Of course, they have a lot of overlap. Econometrics is statistics tailored towards answering economic questions. Machine learning in economics has some similar purpose but uses a massive amount of data. 

Still, we can say that this innovative approach is not built on the same models as econometrics!

ML models focus on prediction problems to minimize forecasting error by trading off bias and variance. 

Also, they can handle vast amounts of data, which may increase productivity without sacrificing interpretation.

The new way of processing, in fact, ‘let the data talk’, though econometrics is efficient to make small but interpretable models. 

Nevertheless, economists have shown resistance to machine learning. 

They have thought that algorithms can’t determine whether a connection between a statistically linked pattern is a coincidence or a cause-and-effect relationship. 

However, there are many benefits of a robotic research assistant. For example, in a study, a machine learning algorithm could use mobile location data from millions of customers to see where people ate lunch! 

In the past, economists have built their data about prices, wages, and inflation on what their research assistants could calculate. 

Now, this new path has the ability and potential to enlarge those data sets significantly and let economists test their models faster than ever. 

We can freely state that machine learning allows economists to work faster. They certainly now have more extensive data sets to solve massive problems. 

Impact of machine learning

The most benefited from machine learning companies were the information firms and online companies. They saw the opportunities of big data before anyone else. It made advantages for these companies – and they became the big winners. 

What we call ‘intelligent automation is a mixture of AI and machine learning – and it is used much more these days.

So, companies who can invest in machine learning can get long-term benefits!

They use computers to take in large amounts of data, process it, and teach themselves new skills using that input. Optimization, processing information, saving labour costs, and increasing productivity is the ideal use of machine learning. 

So basically, this is the way to achieve artificial intelligence (AI); it learns to act without being explicitly programmed.

Takeaway

Machine learning and economics are based on data. It can dramatically increase the accuracy of different models used in economics for increased productivity. AI warrants excellent benefits for productivity and the economy, but it could also simultaneously displace existing jobs as it creates new ones! These are scorching topics today!

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Resources

https://www.protiviti.com/US-en/insights/effects-machine-learning

https://mitsloan.mit.edu/ideas-made-to-matter/why-a-leading-economist-embracing-machine-learning

https://pwc.blogs.com/economics_in_business/2017/07/what-can-machine-learning-add-to-economics-.html

https://www.pwc.com/gx/en/issues/data-and-analytics/artificial-intelligence.html

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