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3 myths about machine learning - debunked

Thursday 16 August 2018 Data Insight & AnalyticsMarketing Technology

Louisa Kierath's picture
By Louisa Kierath

Machine learning and artificial intelligence (AI) have huge potential to help businesses improve efficiency, make more accurate predictions, and expand their business capabilities. And, as the technology becomes cheaper, simpler and more flexible, it’s more accessible than ever.

However, although more organisations are exploring how they might benefit from machine learning, many are holding back on investment due to common misconceptions about the technology. In a business environment that still favours the early adopters, companies that hesitate are likely to miss out on major strategic benefits.

We’ve debunked three of the most common myths around machine learning and AI for business:

 

1. Will machine learning replace people?

When it comes to the public perception of machine learning, the most common myth is that AI is going to replace humans in the workplace. Recent research found that 83% of UK adults believe that AI will one day be smart enough to take over from humans in many roles.

It’s worth noting, however, that this seems to be a general fear rather than a specific one – only 15% think there’s a high chance of losing their own jobs to AI in the next decade.

The truth is, we’re years away from fully autonomous AI, and very few roles are likely to be completely replaced. Machine learning is more useful to us currently as a way to add value to existing roles, expanding our capabilities and making us – and our processes – more efficient.

Rather than workers in knowledge-based roles spending all their time on rote responsibilities, machine learning allows us to give skilled workers more time to focus on sophisticated tasks – the kind that are out of reach of machines. In many industries, machine learning is actually creating new roles, as it’s a high-demand sector that requires highly skilled programmers and data scientists.

 

2. Is machine learning automatically more accurate?

In the long term, there’s huge potential for AI and machine learning to help organisations build strategies based on hyper-accurate predictions – but this won’t happen overnight.

Here, it’s the ‘learning’ component that’s most important to consider. The model won’t be able to deliver flawless accuracy the first time you run it, but it will improve incrementally over time as you give it more data.

And it’s not just historical data that can improve the accuracy of your algorithm; it’s often more useful to focus on giving it data from more diverse sources, such as sales, marketing, HR, and even external factors such as weather patterns. You can use your own datasets, and pull extra information in from other sources, such as IMF Data, The UK Data Centre or the Bureau of Justice.

However, it’s vital that the data you use is carefully prepared. Without high-quality data, machine learning algorithms simply can’t produce high-quality outputs – it’s the classic case of ‘garbage in, garbage out’. For example, if your dataset includes biases that can affect the output, you’ll find that your algorithm will learn and replicate them, which will affect accuracy.

 

3. Do we need a data science team?

Machine learning has been around for decades, but it’s only recently made a resurgence for businesses thanks to new AI technology – so most organisations won’t already have a data science team in place.

Whether you need one or not depends on your business: what you want from your algorithms, the technology you’re using, and whether or not you want to do all the work yourselves.

Some organisations are investing heavily in AI and machine learning as a key part of their business model, and that means building a dedicated data science team is a must. However, for many companies it won’t be necessary to have an entire team – or, in some cases, any data scientists at all.

Many tools on the market will require specialist knowledge and experimentation, but there are tools – including our own CACI Forecaster – that are designed to allow both newcomers to the field and advanced users that have a data science background to reap the benefits of machine learning.

 

Getting started with machine learning and AI

Adopting machine learning and AI into your organisation doesn’t have to be daunting. If you’re not working with a full team of data scientists in-house, it pays to look for intuitive tools and experienced partners that can help you get started faster.
Many of the most common concerns about machine learning and AI are unfounded, and there’s great potential for organisations that find smart applications for it. The key with machine learning is not to get lost in the hype – both positive and negative – it’s all about how you use it.

 

Data Science: Decoded

Register now for our event Data Science: Decoded on the 3rd of October. At this event, you'll learn how brands are using data science to transform the customer experience, and our client British Gas will be speaking about machine learning.

Machine learning and artificial intelligence (AI) have huge potential to help expand business capabilities, but some businesses are holding back due to common misconceptions about the technology. We've debunked three of the most common myths around machine learning and AI for business.

3 myths about machine learning - debunked

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