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Machine Learning, the use case, the fuss, the future

Understanding why and how machine learning (ML) is becoming the use case, the fuss, and the future of the business landscape in today’s data-driven world has become key. In this blog, Ian Jansen Van Rensburg, Director of Solutions Engineering at VMware & Lead Technologist, talks to us about all things ML.

Already, underlying ML technology is embedded within many applications. Smartphones feature virtual assistants, can predict traffic patterns on navigation applications, can detect spam in emails, and even assist in image recognition with face tagging on social networking apps.

The three categories of machine learning:

Supervised ML- Is training with known data sets allows models to learn and grow more accurately over time. This is the most common form of ML in use today.

  • Unsupervised ML- This looks for patterns in unknown data which people might not explicitly be looking for. For example, a programme could analyse online sales data and identify different types of clients making purchases.
  • Reinforcement ML – Trains machines through trial and error to take the best action by establishing a reward system. For instance, reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions.

Gaining momentum
With the likes of Google, Amazon, and Microsoft all launching their Cloud Machine learning platforms, ML is gaining more widespread momentum. And with this comes several business benefits that include improving the efficiency of predictive maintenance in manufacturing, better customer segmentation, more advanced product recommendations, and increased precision of financial roles and models.

ML can also be used for chatbots and predictive text. Product recommendations on online shopping sites and TV series recommendations on streaming apps are all driven by ML.

Essentially, ML gives computers the ability to learn without explicitly being programmed. It uses algorithms to detect hidden patterns in data and make decisions with minimal user intervention which is why and how machine learning (ML) is becoming the future.

Beyond the risk
Just as with any technology, ML poses some risk to a company’s brand and customer trust. Because ML works with data that uses algorithms, it can result in a bias towards a specific race, country, product, religion, and so on. ML developers have been struggling to eliminate these biases that are based on socially sensitive attributes.

To halt the challenge, algorithmic pre-processing can be used to better help ML be able to define what is fair, who is accountable, and how transparency is measured.

Ultimately, with technology continuously evolving, it is essential to remain mindful of its limitations, potential for bias, and the opportunities it has to unlock and drive innovation and new customer experiences.



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