The Data Dilemma Report has revealed 12.5% of staff time is lost during data collection. In a 40-hour workweek, that’s over five valuable hours wasted on similar mundane and repetitive tasks that can be automated. Although machine learning techniques allow for AI solutions to make task automation a reality, there’s still plenty of misinformation out there.

To separate fact from fiction and allow you to take advantage of the powerful potential of machine learning, here are the biggest misconceptions you need to be aware of.

1. Machine Learning and Artificial Intelligence Are Exactly the Same
2. Machine Learning Will Help AI Replace Humans
3. Anyone Can Build a Machine Learning Platform
4. Machine Learning Can’t Predict Unseen Events
5. Machine Learning Is Difficult and Time-Consuming to Implement

1. Machine Learning and Artificial Intelligence Are Exactly the Same

Although Machine Learning (ML) and Artificial Intelligence (AI) are used interchangeably and share certain concepts, they’re different entities. AI is the broader concept of machines carrying out tasks while ML is an application of AI based on the idea that you can give machines data and let them learn by themselves.

It’s primarily used in situations that are easier for machines than humans. Some examples include using probabilistic calculations or executing complex algorithms. So while robotics, computer vision and natural language processes are all aspects which fall under the AI umbrella, ML revolves more around pattern learning, statistics and data prediction.

It’s that valuable computational power of machines which helps ML quickly execute more challenging tasks and uncover patterns that might otherwise be missed by humans. Using those learnings, AI helps to apply where necessary.

2. Machine Learning Will Help AI Replace Humans

There are understandably lots of question marks when it comes to ML. There’s a worry that ML and AI will replace humans entirely. However, the reality is ML is likely to help staff do their jobs, complete tasks to a higher standard with greater efficiency and also save money in the process. It even opens up more creative and strategic opportunities.

For example, Netflix reportedly saved $1 billion in 2017 as a result of its ML algorithm which recommends personalised TV shows and movies to subscribers. In another global example, it’s estimated that ML algorithms will help the healthcare industry save €150 billion by 2025.

ML will allow your staff to put a greater focus on fulfilling the human elements of their role. It’ll let them use their skills to do more creative, strategic and high-levels tasks that genuinely make a difference - basically the stuff they want to do.

Although it’s normal for humans to assume that ML and AI will replace them, there are many areas where human influence, oversight and intervention are needed. Some examples include long-term planning or making decisions that require more context. While technology will continue to advance, the vital human element is mandatory as algorithms might never be 100% perfect.

3. Anyone Can Build a Machine Learning Platform

A quick Google search is likely to highlight many courses and tools to try and help build an extensive ML platform. However, ML is a highly-specialised technique. You need to know how to prepare data, partition it for testing, choose the best algorithms, which heuristics to use and then turn all of this into a reliable and productive system.

The demands then increase further as you need to have the resources to monitor the system continuously to ensure the results remain relevant. If the platform no longer solves the problem you’re trying to fix and newer issues arise, then the process must begin again.

This can also take up your valuable time which can be better utilised in other areas of the business. To allow you to do that, it makes more sense to outsource to a third-party that specialises in this area and has the solutions which your business can benefit from.

4. Machine Learning Can’t Predict Unseen Events

Although AI machines can’t predict unforeseen events that have a major impact, ML can with greater accuracy. While certain issues are extremely difficult or near-impossible to predict, there’s a notion that machines can’t predict these ‘black swan events.’

However, ML has proven this to be false, with the 2008 Housing Crash in the USA being a real-life example.

In this instance, banks relied on flawed financial models until AI systems - thanks to ML - foresaw the crisis.

To detect the unknown, preconfigured rules and models aren’t enough. ML is the only possible way to achieve this as the algorithms can automatically analyse billions of events in real-time to understand unusual features and then identify complex relationships.

5. Machine Learning Is Difficult and Time-Consuming to Implement

Like anything, implementing an essential predictive tool like ML can be a challenge without experts guiding you. However, with the right support, ML can be simple to adopt as it’s primarily focused on supplementing traditional methods by working alongside your business’ existing systems.

It’s an autonomous process and develops algorithms over time as more and more data is used. As a result, there are quicker and more dynamic bespoke models that are constantly developed and easy-to-create.

Implementing ML within your organisation is far less a time-consuming process or burden when you work with experts. When you consider that ML assists existing jobs and leads to dramatic increases in efficiency and productivity, the effort is worth the results.

When it comes to building better human and digital workers, ML is just one example of innovative technology. Others include Robotic Process Automation (RPA) and AI with the three falling under the Intelligent Automation (IA) label. While ML focuses on trying to make decisions without human interaction, RPA completes menial tasks and AI tries to understand the best course of action.

As more businesses implement RPA, there’s going to be more of an overlap between these three cutting-edge technologies. Although, the end goal is the same - digitally transforming businesses to result in effective and innovative process improvements.

However, trying to go on this digital journey alone can be daunting and complex. To better understand how you can reduce costs and streamline processes, we’ve created a guide on how RPA can transform your organisation and eliminate the challenges you face.

Find Out More About Implementing RPA In Your Organisation

For all of the vital RPA information you need, the latest industry trends and how to begin your own RPA journey, go ahead and download our free guide. It’s full of expert advice from RPA specialists, why you need to consider it in the first place, how to actually implement it and much more.

To get your free copy, click on the link below.